Scenario.com MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation3/5
The tool set has clear distinctions between major resource types like assets, models, collections, and workflows, but there is significant overlap within inference tools (e.g., multiple ControlNet variants, img2img variants) that could confuse agents about which specific mode to use. Descriptions help differentiate, but the sheer number of similar-sounding inference endpoints creates ambiguity.
Naming Consistency5/5
Tool names follow a highly consistent verb_noun pattern throughout, using HTTP method prefixes (get, post, put, delete) followed by resource paths with hyphens. This uniform structure makes it predictable and easy to parse, even with the large number of tools.
Tool Count2/5
With 107 tools, the count is excessive for a single server, making it overwhelming and difficult for agents to navigate efficiently. While the domain (AI image generation and management) is broad, many tools could be consolidated (e.g., multiple inference variants) or handled via parameters rather than separate endpoints.
Completeness4/5
The tool surface provides comprehensive CRUD and lifecycle coverage for core resources (assets, models, collections, workflows) and includes a wide range of AI generation and editing operations. Minor gaps exist, such as limited model training management beyond cancel actions, but overall, the set supports most expected workflows in the domain.
Average 2.8/5 across 107 of 107 tools scored. Lowest: 1.5/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. 'Modify' implies a mutation/write operation, but the description doesn't disclose any behavioral traits: no information about permissions needed, whether changes are reversible, what happens on success/failure, rate limits, or what the tool actually returns. This leaves the agent completely in the dark about how this tool behaves beyond the basic implication of modification.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
While technically concise (one short sentence), this is a case of harmful under-specification rather than effective brevity. The single sentence 'Modify the given `presetId`' doesn't earn its place by providing meaningful information - it's essentially empty content that fails to communicate anything useful beyond what's already in the tool name.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a mutation tool with 4 parameters, no annotations, and no output schema. The description is completely inadequate for this complexity level. It doesn't explain what the tool does, when to use it, how parameters interact, what behavior to expect, or what gets returned. For a tool that modifies model presets (which likely involves configuration changes), this description leaves all critical context missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 75% (3 of 4 parameters have descriptions), but the description adds zero parameter semantics. It doesn't explain what 'presetId' refers to, how 'modelId' and 'presetId' relate, what 'originalAssets' controls, or the implications of setting 'isDefault'. The description doesn't compensate for the 25% gap in schema coverage (presetId has no description) and provides no additional context about parameter usage or relationships.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Modify the given `presetId`' is a tautology that essentially restates the tool name. It doesn't specify what modification means (update settings? change values?), what resource is being modified (a model preset), or what fields can be changed. While the name suggests it's about model presets, the description adds no meaningful clarification beyond the name itself.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides absolutely no guidance on when to use this tool versus alternatives. There's no mention of prerequisites, when this tool should be used instead of other preset-related tools (like 'post-model-preset-by-model-id' or 'get-model-presets-by-model-id'), or any context about typical use cases. The agent receives zero usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. 'Delete workflow' implies a destructive mutation, but it fails to specify whether this action is reversible, what permissions are required, what happens to associated resources, or any rate limits. This leaves critical behavioral traits unaddressed for a potentially high-impact operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
While concise with only two words, the description is under-specified rather than efficiently informative. It lacks front-loaded critical details and fails to earn its place by providing meaningful context, making it ineffective despite its brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a destructive tool with no annotations and no output schema, the description is severely incomplete. It doesn't explain the scope of deletion, potential side effects, error conditions, or return values, leaving the agent with insufficient information to invoke the tool safely or interpret results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'workflowId' clearly documented as 'The workflow ID to delete'. The description adds no additional semantic context beyond this, such as format examples or validation rules. Given the high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Delete workflow' is a tautology that merely restates the tool name without adding specificity. It doesn't clarify what 'workflow' means in this context, what resources are affected, or how this differs from other delete operations like 'delete-asset' or 'delete-collections-by-collection-id' among the many sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With sibling tools including various delete operations (e.g., delete-asset, delete-collections-by-collection-id) and other workflow-related tools (e.g., get-workflows-by-workflow-id, put-workflows-by-workflow-id), there's no indication of prerequisites, exclusions, or contextual cues for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral disclosure but offers none. 'Create workflow' implies a write/mutation operation but doesn't specify permissions required, whether the operation is idempotent, what happens on conflict, rate limits, or what the response contains. For a creation tool with zero annotation coverage, this leaves critical behavioral aspects completely undocumented.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is maximally concise at two words, with no wasted language. While severely under-specified, it's not verbose or poorly structured—it simply provides the absolute minimum information possible. Every word ('Create workflow') directly relates to the tool's function, earning its place despite the overall inadequacy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a creation tool with 2 required parameters, no annotations, no output schema, and 0% schema description coverage, the description is completely inadequate. It doesn't explain what a workflow is, what the parameters mean, what happens during creation, what gets returned, or how this differs from related operations. The agent lacks sufficient context to use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description provides zero information about parameters, while the input schema has 0% description coverage (no parameter descriptions in schema). With 2 required parameters (name, description) completely undocumented in both schema and description, the agent has no semantic understanding of what these parameters represent, their format constraints, or how they affect workflow creation. This fails to compensate for the schema's documentation gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Create workflow' is a tautology that essentially restates the tool name 'post-workflows' without adding meaningful specificity. It doesn't clarify what a 'workflow' is in this context, what resources are involved, or how this differs from sibling tools like 'put-workflows-by-workflow-id' (which likely updates existing workflows). While the verb 'Create' is clear, the object 'workflow' remains undefined and indistinguishable from related operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides absolutely no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, appropriate contexts, or when to choose this over sibling tools like 'put-workflows-by-workflow-id' (for updates) or 'get-workflows' (for retrieval). The agent receives no directional information about application scenarios or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It fails to indicate that this is a mutation operation (implied by 'Update'), specify required permissions, describe side effects (e.g., overwriting existing settings), mention rate limits, or detail the response format. For a complex update tool with 12 parameters, this lack of transparency is critical.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single under-specified sentence that fails to convey essential information. While concise, it is not appropriately sized for a tool with 12 parameters and complex functionality. It lacks front-loaded critical details and does not earn its place as a helpful description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (12 parameters, nested objects, no output schema, and no annotations), the description is severely incomplete. It does not explain the update scope, behavioral implications, or usage context. For a mutation tool with extensive configuration options, this minimal description is inadequate and fails to provide necessary operational context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is high (83%), providing detailed documentation for most parameters. The description adds no parameter-specific information beyond implying the 'modelId' is updated. Since the schema carries the burden, the baseline score of 3 is appropriate, though the description does not compensate for the remaining 17% coverage gap or clarify parameter interactions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Update the given `modelId`' is a tautology that restates the tool name without specifying what is being updated. It lacks a clear verb-resource combination (e.g., 'Update model metadata and training parameters') and does not distinguish this tool from sibling PUT tools like 'put-models-by-collection-id' or 'put-models-train-by-model-id'. The purpose is vague and under-specified.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites (e.g., model status requirements), exclusions, or compare it to sibling tools like 'put-models-train-by-model-id' (for training actions) or 'post-models' (for creation). This leaves the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden but offers no behavioral details. It does not disclose whether this is a read-only or destructive operation, authentication requirements, rate limits, side effects (e.g., job creation, resource consumption), or expected outcomes. This leaves the agent with minimal context for safe invocation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single sentence 'Run a workflow', which is front-loaded and wastes no words. While under-specified, it is not verbose or poorly structured, earning full marks for brevity and clarity within its limited scope.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (inferred from sibling tools involving workflows and jobs), lack of annotations, 0% schema coverage, and no output schema, the description is highly incomplete. It does not cover behavior, parameters, usage context, or expected results, making it inadequate for effective tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 1 parameter with 0% description coverage, and the description adds no meaning beyond the schema. It does not explain what 'workflowId' represents (e.g., identifier format, source, constraints) or how it affects the 'run' operation. With low schema coverage, the description fails to compensate, leaving parameters undocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Run a workflow' restates the tool name 'put-workflow-run-by-workflow-id' in simpler terms, making it tautological. It specifies the verb 'run' and resource 'workflow', but lacks specificity about what 'run' entails (e.g., execution, triggering, processing) and does not differentiate from siblings like 'post-workflows' or 'get-workflows-by-workflow-id'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It does not mention prerequisites (e.g., needing an existing workflow), exclusions, or comparisons to sibling tools such as 'post-workflows' (which might create workflows) or 'get-workflows-by-workflow-id' (which might retrieve workflow details).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. 'Caption image(s)' gives no information about what the tool does beyond the basic action—no details on output format, rate limits, permissions required, or side effects. This is inadequate for a tool with 10 parameters and no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with two words, 'Caption image(s)', which is front-loaded and wastes no space. However, this brevity comes at the cost of clarity and completeness, but as per the dimension's focus, it is structurally efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (10 parameters, 60% schema coverage, no annotations, no output schema), the description is completely inadequate. It does not explain what the tool returns, how it behaves, or provide any context beyond the minimal action, failing to meet the needs for effective tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 60%, but the description adds no parameter semantics beyond the tool name. It does not explain what 'images' should contain, how 'dryRun' affects behavior, or the purpose of other parameters like 'ensureIPCleared' or 'unwantedSequences'. The description fails to compensate for the 40% coverage gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Caption image(s)' restates the tool name 'post-caption-inferences' in a slightly different phrasing, making it tautological. It lacks specificity about what 'caption' entails (e.g., generating descriptive text for images) and does not distinguish this tool from sibling tools like 'post-describe-style-inferences' or other image processing tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling tools for image processing (e.g., 'post-img2img-inferences', 'post-detect-inferences'), there is no indication of context, prerequisites, or exclusions, leaving the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It only states the action ('patch an asset with an image') without mentioning effects (e.g., whether it modifies the original asset, creates a new one, requires permissions, has rate limits, or returns a result). For a tool with 9 parameters and no output schema, this is a critical omission.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It's front-loaded and appropriately sized for its content, though it's under-specified rather than concise. Given the scoring criteria, it earns full points for brevity and structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given high complexity (9 parameters, nested objects, no annotations, no output schema), the description is severely incomplete. It doesn't explain what 'patch' entails, the tool's behavior, output format, or parameter roles. For a mutation tool with extensive input schema, this minimal description is inadequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 56%, so the description should compensate for gaps. It mentions 'an image' but doesn't explain parameters like 'originalAssets', 'dryRun', 'patch', 'backgroundColor', 'format', 'position', 'allowOverflow', or 'crop'. The description adds minimal value beyond the schema, failing to clarify the purpose or interactions of these parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Patch an asset with an image' states a verb ('patch') and resource ('asset'), but it's vague about what 'patch' means in this context (e.g., merging, overlaying, modifying). It doesn't distinguish from siblings like 'post-img2img-inferences' or 'post-inpaint-inferences', which might involve similar image operations. The purpose is minimally stated but lacks specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. With many sibling tools for image processing (e.g., 'post-img2img-inferences', 'post-inpaint-inferences'), the description offers no context, prerequisites, or exclusions. This leaves the agent without direction on appropriate usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'Generate, complete or invent' but doesn't disclose any behavioral traits: no information about whether this is a read or write operation, what permissions are needed, rate limits, output format, or side effects. For a tool with 12 parameters and no output schema, this is critically inadequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just one sentence with no wasted words. It's front-loaded with the core purpose, though this brevity comes at the cost of completeness. Every word earns its place, making it structurally efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (12 parameters, no annotations, no output schema), the description is severely incomplete. It doesn't explain what the tool actually does beyond vague verbs, doesn't provide usage context, and offers no behavioral details. For a tool that likely generates AI prompts with multiple modes and parameters, this is inadequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 75%, so the schema documents most parameters well. The description adds no parameter-specific information beyond the schema. However, with high schema coverage, the baseline is 3 as the schema does the heavy lifting, though the description doesn't compensate for the remaining 25% gap (e.g., 'dryRun' is undocumented in both).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Generate, complete or invent new prompts' restates the tool name 'post-prompt-inferences' in slightly different words, making it tautological. It doesn't specify what kind of prompts (e.g., for AI models, creative writing, etc.) or what resource is being acted upon, and it doesn't distinguish this from sibling tools like 'post-prompt-editing-inferences' or 'post-caption-inferences'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling tools for different inference types (e.g., 'post-txt2img-inferences', 'post-img2img-inferences', 'post-prompt-editing-inferences'), there's no indication of when this tool is appropriate or what distinguishes it from other prompt-related operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. 'Delete an image' implies a destructive operation, but it lacks details on permissions required, whether deletions are permanent or reversible, rate limits, or error handling. For a mutation tool with zero annotation coverage, this is a significant gap, as it doesn't inform the agent about critical behavioral aspects beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with just three words, making it front-loaded and waste-free. It directly states the action without unnecessary elaboration, which is efficient for a simple tool. However, this conciseness comes at the cost of completeness, but for this dimension alone, it scores high due to its brevity and clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity as a destructive operation with no annotations and no output schema, the description is inadequate. It fails to explain what happens upon deletion, potential side effects, or return values. For a mutation tool, this lack of context leaves the agent underinformed, scoring low as it doesn't provide enough information for safe and effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for both parameters ('modelId' and 'ids'). The description adds no additional meaning beyond what the schema provides, such as format examples or constraints. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Delete an image' restates the tool name's core action ('delete') and object ('image'), making it tautological. It fails to specify that it deletes images associated with a particular model ID, which is a key detail from the input schema. While it indicates the action, it lacks specificity about the resource scope, scoring low for not distinguishing from siblings like 'delete-asset' or 'delete-models-training-images-by-model-id-and-training-image-id'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, such as needing a model ID and image IDs, or compare it to sibling tools like 'delete-asset' or 'delete-models-by-model-id'. Without any context for selection, it leaves the agent guessing, resulting in a minimal score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions editing an image but does not clarify critical behaviors: whether this is a generative AI operation, if it modifies the original asset or creates a new one, potential rate limits, authentication needs, or output format. The description is too minimal to inform safe and effective use, especially for a complex tool with 17 parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—a single sentence with no wasted words. It is front-loaded and to the point, though this brevity contributes to its inadequacy in other dimensions. Every sentence (here, just one) earns its place by stating the core action, but it lacks necessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (17 parameters, no annotations, no output schema), the description is incomplete. It fails to explain the tool's purpose in context, behavioral traits, or usage scenarios. While the schema covers parameters, the description does not add value to help an agent understand when or how to use this tool effectively, making it insufficient for such a multifaceted operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is high at 82%, so the schema already documents most parameters well. The description adds no parameter-specific information beyond the schema—it does not explain key parameters like 'prompt', 'image', or model-specific options. However, with high schema coverage, the baseline score is 3, as the description does not compensate but also does not detract from the existing documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Edit an image with a prompt' states the basic action but is vague and tautological—it essentially restates the tool name 'post-prompt-editing-inferences' without specifying what kind of editing occurs (e.g., generative AI-based transformation, inpainting, style transfer) or distinguishing it from sibling tools like 'post-img2img-inferences' or 'post-inpaint-inferences'. It lacks specificity about the resource being edited (an image asset) and the mechanism (prompt-driven AI editing).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling tools for image manipulation (e.g., 'post-img2img-inferences', 'post-inpaint-inferences', 'post-generative-fill-inferences'), there is no indication of this tool's specific role, prerequisites, or exclusions. It fails to help an agent choose appropriately among similar tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It only mentions 'advanced vectorization', lacking details on permissions, side effects (e.g., whether it modifies or creates new assets), rate limits, or output format. For a complex tool with 14 parameters, this is insufficient to inform safe and effective use.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It is appropriately sized and front-loaded, though it could benefit from more detail. Every word earns its place, making it highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (14 parameters, no annotations, no output schema), the description is inadequate. It doesn't explain the vectorization process, output format, or behavioral context, leaving significant gaps for the agent to infer usage. A more complete description would address these aspects to complement the rich schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 93%, so the schema provides detailed parameter documentation. The description adds no parameter-specific information beyond the schema, such as default values or interactions between parameters. However, with high schema coverage, a baseline score of 3 is appropriate as the schema carries the burden.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Advanced vectorization of an image' states the general action but lacks specificity. It doesn't clarify what 'vectorization' entails (e.g., converting raster to vector graphics) or differentiate it from siblings like 'post-pixelate-inferences' or 'post-upscale-inferences', which also process images. This is vague and borderline tautological with the tool name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. With many sibling tools for image processing (e.g., 'post-pixelate-inferences', 'post-upscale-inferences'), the description fails to indicate scenarios for vectorization over other methods, prerequisites, or exclusions. This leaves the agent without context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. 'Get workflow by ID' implies a read-only operation, but it doesn't specify whether this requires authentication, what happens if the ID doesn't exist (e.g., error handling), or any rate limits. For a tool with zero annotation coverage, this leaves significant behavioral gaps unaddressed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just three words ('Get workflow by ID'), with zero wasted verbiage. It's front-loaded and efficiently communicates the core action, though this brevity comes at the cost of completeness in other dimensions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema, the description is inadequate. It doesn't explain what a 'workflow' represents in this system, what data is returned, or any behavioral constraints. Given the complexity implied by sibling tools (e.g., workflow runs, posts, puts), more context is needed for the agent to use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'workflowId' clearly documented as 'The workflow ID to retrieve'. The description doesn't add any meaningful semantic context beyond what the schema already provides (e.g., format examples, ID sourcing), so it meets the baseline score of 3 where the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get workflow by ID' is a tautology that essentially restates the tool name 'get-workflows-by-workflow-id' without adding meaningful clarification. While it correctly identifies the verb ('Get') and resource ('workflow'), it doesn't specify what 'workflow' means in this context or what information is retrieved, making it minimally informative beyond the obvious.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. Given the sibling tools include 'get-workflows' (which likely lists workflows) and 'delete-workflows-by-workflow-id' (which deletes by ID), there's no indication of when this retrieval-by-ID tool is appropriate versus those other options, leaving the agent without contextual usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'Trigger an action on upload,' which implies a mutation or state change, but doesn't specify whether this is idempotent, what permissions are required, if it's asynchronous, or what happens upon completion. For a tool with potential side effects, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single sentence, 'Trigger an action on upload,' which is front-loaded and wastes no words. While it may be under-specified, it's not verbose or poorly structured, earning full marks for brevity and clarity within its limited scope.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of triggering an action (likely a mutation), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'action' entails, what the expected outcome is, or any error conditions. For a tool that could affect system state, this leaves critical gaps for an agent to understand its full context and behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters (uploadId and action) with clear descriptions and an enum for action. The description adds no additional meaning beyond what's in the schema, such as explaining the relationship between parameters or typical use cases. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Trigger an action on upload' is a tautology that essentially restates the tool name 'post-uploads-action' without adding meaningful specificity. It mentions 'upload' as the resource but doesn't specify what kind of action is triggered or what the outcome is, making it vague compared to more descriptive sibling tools like 'post-uploads' or 'post-job-action-by-job-id'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, context, or exclusions, such as when an upload must be in a certain state or what other tools might be related. With many sibling tools available, this lack of differentiation leaves the agent guessing about appropriate usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It implies a read-only operation ('Get'), but doesn't disclose behavioral traits like authentication needs, rate limits, error handling, or what 'job data' includes (e.g., status, metadata). For a tool with zero annotation coverage, this is inadequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It's front-loaded and appropriately sized for a simple tool, though its brevity contributes to gaps in other dimensions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations, 0% schema coverage, and no output schema, the description is incomplete. It doesn't explain what 'job data' returns, how errors are handled, or usage context. For a tool in a complex server with many siblings, this leaves the agent under-informed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, with one required parameter 'jobId' undocumented in the schema. The description adds minimal semantics by implying 'jobId' is used to fetch data, but doesn't specify format, constraints, or examples. It doesn't compensate for the coverage gap, leaving the parameter poorly understood.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get job data by job ID' clearly states the verb ('Get') and resource ('job data'), making the purpose understandable. However, it's somewhat vague about what 'job data' entails and doesn't differentiate from sibling tools like 'get-jobs' (which likely lists multiple jobs). It avoids tautology but lacks specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, context, or exclusions, leaving the agent to infer usage from the name alone. This is a significant gap for a tool in a server with many similar-sounding siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves asset details, implying a read-only operation, but does not specify aspects like authentication requirements, rate limits, error handling, or what 'details' include. This is insufficient for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, straightforward sentence that efficiently conveys the core action. It is front-loaded with the essential information, though it could be slightly more informative without sacrificing brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema, the description is inadequate. It lacks details on behavioral traits, return values, or differentiation from siblings. While the schema covers the single parameter, the overall context for effective tool use is incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'assetId' documented as 'The asset ID to retrieve'. The description does not add any additional meaning beyond this, such as format examples or constraints. Given the high schema coverage, a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get the details of an asset' clearly states the verb ('Get') and resource ('asset'), making the purpose understandable. However, it does not distinguish this tool from its sibling 'get-assets-by-asset-id', which appears to serve a similar function based on the name, leaving ambiguity about when to use one over the other.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. Given the presence of sibling tools like 'get-assets-by-asset-id' and 'get-assets', there is no indication of specific contexts, prerequisites, or exclusions for this tool, leaving usage unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. While 'Get' implies a read operation, it doesn't disclose important behavioral aspects like authentication requirements, rate limits, error conditions, or what 'public' means in this context. The description is minimal and lacks necessary operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with just one sentence that directly states the tool's function. While perhaps too minimal, it contains no wasted words and is front-loaded with the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema, the description is inadequate. It doesn't explain what 'public' means, what details are returned, or how this differs from non-public variants. Given the complexity implied by the sibling tools and lack of structured documentation, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the single parameter 'modelId' clearly documented in the schema. The description adds no additional parameter information beyond what the schema already provides, which meets the baseline expectation when schema coverage is complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool retrieves details for a given model ID, which is a clear verb+resource combination. However, it doesn't distinguish this from similar sibling tools like 'get-models-by-model-id' or 'get-public-models', leaving ambiguity about what makes this 'public' version different.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives. With multiple similar tools in the sibling list (get-models-by-model-id, get-public-models, get-models), the description offers no context about appropriate use cases, prerequisites, or distinctions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'describes' style, implying a read-only analysis, but doesn't disclose critical traits like whether it modifies data, requires authentication, has rate limits, or what the output format is (e.g., text description, scores). For a tool with 9 parameters and no annotations, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words, making it appropriately sized. However, it's not front-loaded with critical details (e.g., output type or key parameters), so it's concise but could be more structured for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (9 parameters, no output schema, no annotations), the description is incomplete. It doesn't explain what 'style' entails, how results are returned, or the tool's behavior, leaving significant gaps for an AI agent to understand and use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 56%, with some parameters like 'seed' and 'temperature' well-documented in the schema. The description adds no meaning beyond the schema, as it doesn't explain how parameters like 'images', 'modelId', or 'unwantedSequences' relate to style description. With moderate schema coverage, the baseline is 3, but the description fails to compensate for the 44% undocumented parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Describe the style of the given images or models' states a clear verb ('describe') and resource ('style of images or models'), but it's vague about what 'style' means (e.g., artistic, technical, or other attributes) and doesn't distinguish it from siblings like 'post-caption-inferences' or 'post-detect-inferences', which might also analyze images. It's adequate but lacks specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, such as other inference tools in the sibling list (e.g., 'post-caption-inferences' for general descriptions or 'post-detect-inferences' for object detection). There's no mention of prerequisites, context, or exclusions, leaving usage unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'transforming visual data' which implies a processing operation, but doesn't specify whether this is read-only or mutating, what the output format is, performance characteristics, error conditions, or authentication needs. The description is too high-level to provide meaningful behavioral context for safe invocation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that efficiently states the tool's purpose. It's appropriately sized for what it communicates, though it could be more informative. There's no wasted verbiage or unnecessary elaboration, making it structurally sound if minimal.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 11 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what 'mode maps' are, what the transformation produces, error handling, or practical use cases. Given the complexity implied by multiple modality options and numerous parameters, the description should provide more context about the tool's behavior and output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 91%, so the schema already documents most parameters well. The description adds no additional parameter information beyond what's in the schema - it doesn't explain how parameters interact, provide examples, or clarify edge cases. With high schema coverage, the baseline score of 3 is appropriate since the description doesn't add value but also doesn't detract from the schema's documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool transforms visual data into mode maps for advanced precision in image generation, which gives a general purpose. However, it's vague about what 'mode maps' are and doesn't clearly distinguish this from sibling tools like 'post-segment-inferences' or 'post-controlnet-inferences' that also process images. The description lacks specificity about the exact transformation being performed.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, ideal use cases, or contrast with sibling tools like 'post-caption-inferences' or 'post-describe-style-inferences'. Usage is implied through the mention of 'image generation' and 'mode maps', but no concrete scenarios or exclusions are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the action is 'cancel' but doesn't disclose behavioral traits: whether this is destructive (likely yes, as it stops training), if it requires specific permissions, rate limits, or what the response looks like (e.g., success/failure message). The description is minimal and misses key operational details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise with a single sentence, front-loaded with the key action. There's no wasted text, but it might be overly terse given the lack of behavioral context. It efficiently states the purpose without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a mutation tool (canceling training is likely destructive), the description is incomplete. It doesn't cover success/error responses, side effects, or prerequisites. For a tool that modifies system state, more context is needed to use it safely and effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters fully. The description adds no additional meaning beyond what's in the schema (e.g., it doesn't explain modelId format or action implications). Baseline score of 3 is appropriate as the schema handles parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the action ('trigger') and resource ('model training'), but is vague about scope and effect. It mentions 'cancel' as the action, but doesn't clarify if this stops training permanently or temporarily, or what happens to the model. It distinguishes from siblings by focusing on training actions, but lacks specificity about the outcome.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., model must be in training state), exclusions, or related tools like 'put-models-train-by-model-id' for starting training. Usage is implied only by the action name 'cancel'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions a constraint (mutual exclusivity of 'image' and 'images') but fails to disclose critical behavioral traits such as whether this is a read-only operation, if it requires authentication, rate limits, pagination behavior, or what the return format looks like. This is inadequate for a search tool with 13 parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, with three sentences that directly address key points (purpose, requirement, and exclusivity). There is no wasted text, making it efficient, though it could benefit from more structure or bullet points for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (13 parameters, no annotations, no output schema), the description is incomplete. It lacks information on behavioral aspects like authentication needs, rate limits, pagination, and return values, which are crucial for a search tool. The high schema coverage helps but does not compensate for these gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 85%, so the schema already documents most parameters well. The description adds minimal value by noting the requirement for at least one of 'query,' 'filter,' 'image,' or 'images' and the mutual exclusivity of 'image' and 'images,' but does not provide additional syntax, format details, or explanations beyond what the schema offers.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool's purpose as 'Search for assets,' which is clear but vague. It specifies the verb ('Search') and resource ('assets'), but does not differentiate from sibling tools like 'get-assets' or 'post-search-models,' leaving ambiguity about scope or method.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal guidance by stating that at least one of 'query,' 'filter,' 'image,' or 'images' must have a value, and that 'image' and 'images' are mutually exclusive. However, it lacks context on when to use this tool versus alternatives like 'get-assets' or 'post-search-models,' and does not mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that at least one field must be populated and that 'image' and 'images' are mutually exclusive, which adds some behavioral context. However, it doesn't describe what the search returns, whether it's paginated, if it requires authentication, or any rate limits. For a search tool with 13 parameters and no annotations, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with three sentences that directly address constraints and purpose. It's front-loaded with the main action ('Search for models') and follows with necessary conditions. There's no wasted text, though it could be slightly more structured by separating constraints into bullet points for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (13 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the search behavior, result format, or how parameters interact (e.g., pagination with 'offset'/'limit' or 'page'/'hitsPerPage'). For a search tool with rich input options, more context is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 77%, which is high, so the baseline is 3 even without parameter details in the description. The description adds minimal value by noting the requirement for at least one field and the mutual exclusivity of 'image' and 'images,' but it doesn't explain the semantics of key parameters like 'query' or 'filter' beyond what the schema provides. This meets the baseline but doesn't compensate for the 23% coverage gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool's purpose as 'Search for models,' which is a clear verb+resource combination. However, it doesn't distinguish this tool from sibling tools like 'get-models' or 'get-public-models,' leaving ambiguity about when to use this search tool versus simple retrieval tools. The purpose is clear but lacks sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal usage guidance by stating that at least one field must have a value and that 'image' and 'images' are mutually exclusive. However, it doesn't explain when to use this tool versus alternatives like 'get-models' or 'post-search-assets,' nor does it provide context about prerequisites or typical use cases. The guidance is limited to parameter constraints without broader usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions actions ('Add/delete/sort') which imply mutations, but doesn't specify permissions required, whether changes are reversible, rate limits, or what the output looks like (no output schema). This leaves critical behavioral traits undocumented for a tool that modifies data.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the key actions. However, it could be more structured by clarifying the relationship between actions and parameters, but it earns high marks for brevity and lack of redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (multiple actions, 3 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the resource ('examples'), how actions are determined (e.g., via parameters or context), or behavioral aspects like side effects. For a mutation tool with moderate schema coverage, this leaves significant gaps in understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 67% (2 out of 3 parameters described), with 'originalAssets' having a description and 'modelId' described, but 'assetIds' lacks a description. The tool description adds no parameter semantics beyond the schema—it doesn't explain what 'assetIds' represent, how sorting works, or the effect of 'originalAssets'. Since schema coverage is moderate, the baseline is 3, as the description doesn't compensate for the gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the action ('Add/delete/sort') and resource ('examples of the given modelId'), but it's vague about what 'examples' are and doesn't distinguish from siblings like 'get-models-examples-by-model-id' (which presumably retrieves examples) or 'put-models-training-images-by-model-id' (which might handle similar assets). The purpose is understandable but lacks specificity about the resource type.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a valid modelId), exclusions, or how it differs from sibling tools like 'put-models-training-images-by-model-id' or 'delete-models-examples-by-model-id' (if it exists, though not listed). The description implies a multi-action tool but offers no context for choosing among add, delete, or sort.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. 'Update workflow' implies a mutation operation, but it doesn't specify permissions required, whether changes are reversible, error handling, or response format. This is inadequate for a mutation tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with just two words, 'Update workflow', which is front-loaded and wastes no space. However, this conciseness comes at the cost of under-specification, but as per scoring rules, it earns full points for brevity and clarity within its limited scope.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity as a mutation operation with 3 parameters, 0% schema coverage, no annotations, and no output schema, the description is incomplete. It lacks essential details about behavior, parameters, and usage context, making it insufficient for effective tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 3 parameters with 0% description coverage, and the tool description adds no information about parameters. It doesn't explain what 'workflowId', 'name', or 'description' represent, their formats, or constraints, failing to compensate for the schema's lack of documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Update workflow' clearly states the verb ('Update') and resource ('workflow'), but it's vague about what specific aspects are updated. It doesn't distinguish this tool from sibling tools like 'put-asset-by-asset-id' or 'put-models-by-model-id' beyond the resource type, lacking specificity about workflow-specific operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, such as needing an existing workflow ID, or differentiate it from related tools like 'post-workflows' (create) or 'delete-workflows-by-workflow-id' (delete), leaving usage context unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. 'List all models' implies a read-only operation, but it does not specify critical behaviors like pagination handling (implied by parameters but not described), rate limits, authentication requirements, or error conditions. For a tool with 8 parameters and no annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at three words, front-loading the core action ('List all models') with zero wasted text. It efficiently communicates the basic purpose without unnecessary elaboration, making it easy to parse quickly. Every word earns its place by directly stating the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (8 parameters, no annotations, no output schema), the description is incomplete. It lacks information on behavioral traits (e.g., pagination, auth), usage context relative to siblings, and output format. While the schema covers parameters well, the description does not compensate for missing annotations or provide sufficient guidance for effective tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, thoroughly documenting all 8 parameters (e.g., privacy, pageSize, collectionId). The description adds no additional meaning beyond the schema, as it does not mention any parameters or their interactions. According to the rules, with high schema coverage (>80%), the baseline score is 3, reflecting adequate but unenhanced parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'List all models' clearly states the verb ('List') and resource ('models'), providing a basic purpose. However, it lacks specificity about scope (e.g., all models in what context?) and does not distinguish this tool from sibling tools like 'get-models-by-model-id' or 'get-public-models', which offer more targeted retrieval. This makes it vague in comparison to alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'get-models-by-model-id' (for specific models), 'get-public-models' (for public models only), and 'post-search-models' (for filtered searches), there is no indication of when this broad listing tool is preferred, such as for bulk retrieval or initial exploration. This absence of context leaves usage unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the basic action without details on permissions, rate limits, error handling, or response format. For a read operation, this lack of context (e.g., whether it's public or requires authentication) is a significant gap, though it doesn't contradict any annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste—it directly states the tool's purpose without fluff. It's appropriately front-loaded and concise, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for a tool with potential complexity. It doesn't explain what 'description' includes (e.g., text, metadata, or structured data), how results are formatted, or any behavioral traits like caching or errors. This leaves the agent under-informed for proper invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for both parameters: 'modelId' as the identifier to retrieve and 'originalAssets' for returning untransformed assets. The description adds no extra meaning beyond the schema, such as explaining when to use 'originalAssets' or format details. Baseline 3 is appropriate since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get the description of the given `modelId`' clearly states the action (get) and resource (description), but it's vague about what 'description' entails (e.g., metadata, text summary, or technical specs). It doesn't distinguish from siblings like 'get-models-by-model-id' or 'get-models-examples-by-model-id', leaving ambiguity about what specific data is retrieved.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. For example, it doesn't clarify if this is for retrieving textual descriptions only, as opposed to 'get-models-by-model-id' which might return full model details. There's no mention of prerequisites or exclusions, leaving the agent to infer usage from context alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. 'List all public assets' implies a read-only operation, but it doesn't specify whether this is a safe, non-destructive action, mention pagination behavior (implied by parameters like 'pageSize' and 'paginationToken'), rate limits, or authentication needs. The description is too vague to inform the agent adequately about how the tool behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single sentence, 'List all public assets', which is front-loaded and wastes no words. It directly states the tool's action without unnecessary elaboration, making it efficient for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (13 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what 'public assets' are, how results are structured, or behavioral aspects like pagination. While the schema covers parameters well, the description fails to provide necessary context for effective tool use, especially for a list operation with many filtering options.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, detailing all 13 parameters with clear explanations, enums, and constraints. The description adds no additional parameter information beyond the schema, so it doesn't enhance understanding. According to the rules, with high schema coverage (>80%), the baseline score is 3, as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'List all public assets' clearly states the verb ('List') and resource ('public assets'), making the purpose understandable. However, it lacks specificity about what 'public assets' are in this context (e.g., media files, models, etc.) and doesn't distinguish it from sibling tools like 'get-assets' or 'get-public-assets-by-asset-id', which offer different scopes or functionalities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, such as authentication requirements, or compare it to sibling tools like 'get-assets' (which might list private assets) or 'get-public-assets-by-asset-id' (for a single asset). This leaves the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. 'List workflows' implies a read-only operation, but it doesn't disclose behavioral traits like pagination behavior (implied by parameters but not stated), rate limits, authentication needs, or what the output looks like. For a tool with parameters and no output schema, this leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at two words, with zero wasted language. It's front-loaded with the core action and resource. While it may be too brief for completeness, it earns full marks for conciseness as every word serves a clear purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 3 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain the purpose of parameters like 'privacy', pagination behavior, or what the list output contains. For a list operation with filtering options, more context is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 67% (2 of 3 parameters have descriptions). The description adds no parameter information beyond the schema. With moderate coverage, the baseline is 3 as the schema does most work, but the description doesn't compensate for the undocumented 'privacy' parameter or provide context on how parameters interact.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'List workflows' clearly states the verb ('List') and resource ('workflows'), making the basic purpose understandable. However, it lacks specificity about scope or filtering capabilities, and doesn't differentiate from sibling tools like 'get-workflows-by-workflow-id' which retrieves a specific workflow. It's adequate but minimal.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention that this lists multiple workflows while 'get-workflows-by-workflow-id' retrieves a single one, nor does it suggest usage contexts like browsing versus detailed lookup. The agent must infer usage from tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. 'Lock a canvas' implies a mutation (state change to locked), but doesn't disclose behavioral traits such as required permissions, whether locking is reversible, effects on other users, rate limits, or error conditions. The description is minimal and lacks critical context for a mutation operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with 'Lock a canvas'—a single, front-loaded sentence that directly states the action. There is zero waste or redundancy, making it efficient for quick understanding, though it may be overly terse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a mutation with 3 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavior, return values, error handling, and usage context. For a tool that modifies asset state, more information is needed to ensure correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters are documented in the schema. The description adds no meaning beyond the schema—it doesn't explain parameter relationships, default behaviors, or usage examples. With high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Lock a canvas' states the action and resource but is vague about scope and mechanism. It uses the verb 'lock' which implies a state change, but doesn't specify what locking entails (e.g., preventing edits, setting permissions). It distinguishes from sibling 'unlock-asset-by-asset-id' by opposite action, but doesn't differentiate from other asset-related tools like 'put-asset-by-asset-id' or 'get-assets-by-asset-id' in terms of purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives is provided. The description doesn't mention prerequisites (e.g., needing asset ID, expiration time), exclusions, or compare to siblings like 'unlock-asset-by-asset-id' or other asset modification tools. Usage is implied only through the action 'lock', but no explicit context or scenarios are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states 'Get embeddings' which implies a read operation, but doesn't disclose behavioral traits like authentication needs, rate limits, response format, or whether it's idempotent. For a tool with no annotation coverage, this leaves significant gaps in understanding its operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It's front-loaded and directly states the tool's purpose without unnecessary elaboration, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of an embedding tool with 3 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what embeddings are, how they're used, the output format, or error handling. For a tool that likely returns vector data, more context is needed to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 67% (2 out of 3 parameters have descriptions). The description mentions 'text' but doesn't add meaning beyond the schema's description for 'text'. It doesn't explain 'originalAssets' or 'dryRun', leaving them partially undocumented. With moderate schema coverage, the description provides minimal additional parameter context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get embeddings from text' clearly states the action (get) and resource (embeddings from text), but it's vague about what embeddings are or their purpose. It doesn't distinguish this tool from other 'post-*' inference tools like 'post-caption-inferences' or 'post-detect-inferences', which also process text or images to produce outputs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. With many sibling tools for different types of inferences (e.g., captioning, detection, translation), the description lacks context about specific use cases, prerequisites, or comparisons to other embedding-related tools if any exist.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states 'Create a new preset', implying a write/mutation operation, but doesn't disclose behavioral traits such as required permissions, whether it's idempotent, rate limits, or what happens on success/failure (e.g., returns a preset ID). This leaves significant gaps for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste—it directly states the tool's purpose without fluff. It's appropriately sized and front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given this is a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't cover behavioral aspects (e.g., side effects, error handling) or output details (e.g., what's returned after creation). The 100% schema coverage helps with inputs, but overall context is lacking for safe and effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters are well-documented in the schema. The description adds no meaning beyond the schema—it doesn't explain how parameters interact (e.g., 'modelId' ties to an existing model, 'inferenceId' for image generation) or provide usage examples. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Create a new preset for the given `modelId`' clearly states the action (create) and resource (preset), but it's vague about what a 'preset' entails (e.g., configuration settings, image generation parameters) and doesn't differentiate from sibling tools like 'post-models' or 'put-model-preset-by-model-id-and-preset-id'. It avoids tautology but lacks specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing model), exclusions, or compare to similar tools like 'put-model-preset-by-model-id-and-preset-id' (update) or 'get-model-presets-by-model-id' (list). The description implies usage only through the verb 'create' without context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Advanced pixelization' implies a mutation operation that transforms an image, but it doesn't specify whether this is destructive to the original, requires specific permissions, has rate limits, or what the output looks like (e.g., returns a new asset ID). The description lacks critical behavioral context for a tool with 8 parameters and no output schema, leaving the agent uncertain about side effects and results.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'Advanced pixelization of an image.' It's front-loaded with the core action and resource, with zero wasted words. While it could be more informative, it meets conciseness criteria by avoiding redundancy and being appropriately sized for a tool name that hints at functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (8 parameters, 3 required, no output schema, and no annotations), the description is incomplete. It doesn't explain the transformation's output, error conditions, or behavioral traits. For an image processing tool with multiple options like 'removeBackground' and 'colorPalette', more context is needed to guide the agent effectively. The description fails to compensate for the lack of structured data, leaving significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 75%, with 6 of 8 parameters having descriptions (e.g., 'image', 'pixelGridSize', 'removeNoise'). The description adds no parameter-specific information beyond the schema. For parameters without schema descriptions ('dryRun', 'colorPalette'), the description doesn't compensate. Given the high schema coverage, the baseline is 3, as the description doesn't enhance parameter understanding but doesn't detract either.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Advanced pixelization of an image' states the tool's purpose (pixelization) but is vague about what 'advanced' entails. It distinguishes from siblings like 'post-upscale-inferences' or 'post-vectorize-inferences' by specifying pixelization, but doesn't clarify how it differs from other image transformation tools (e.g., 'post-img2img-inferences') in terms of output or method. The verb 'pixelization' is clear, but the modifier 'advanced' lacks specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling tools for image processing (e.g., 'post-upscale-inferences', 'post-remove-background-inferences'), there's no indication of scenarios where pixelization is preferred, prerequisites, or exclusions. The agent must infer usage from the tool name and parameters alone, which is insufficient for effective selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'advanced' but doesn't disclose behavioral traits like whether it's a read-only or destructive operation, authentication needs, rate limits, or output format details. The description is too minimal to inform the agent about how the tool behaves beyond its basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It's front-loaded and appropriately sized for the tool's complexity, making it easy to parse quickly. Every word earns its place by conveying the core function without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a mutation tool (implied by 'remove-background'), the description is incomplete. It lacks information on what the tool returns, error conditions, or side effects. For a tool with 5 parameters and complex image processing, this minimal description leaves significant gaps in understanding its full context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 80%, providing good documentation for most parameters. The description adds no parameter semantics beyond the schema, which already includes details like asset IDs, background colors, and formats. With high schema coverage, the baseline is 3, as the description doesn't compensate or add extra meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Advanced remove-background of an image' states the verb (remove-background) and resource (image), but is vague about what 'advanced' means compared to other tools. It doesn't distinguish from siblings like 'post-segment-inferences' or 'post-generative-fill-inferences' that might also manipulate image backgrounds. The purpose is clear but lacks specificity and differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives is provided. With many sibling tools for image processing (e.g., 'post-segment-inferences', 'post-generative-fill-inferences'), the description offers no context on use cases, prerequisites, or exclusions. It merely states what it does without helping the agent choose appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. The description only states the basic function without mentioning critical behaviors: it doesn't indicate if this is a read-only or mutating operation, what permissions are required, rate limits, error handling, or output format. For a tool with 7 parameters including AI-related settings like temperature and seed, this lack of behavioral context is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at one sentence with no wasted words. It's front-loaded with the core purpose. While it may be too brief for completeness, it earns full points for conciseness as every word contributes directly to stating the tool's function without redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (7 parameters including AI generation settings), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns, how errors are handled, or the relationship between translation and parameters like 'images' or 'seed'. For an inference tool with multiple configuration options, more context about behavior and results is needed to be adequately complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 71% (5 of 7 parameters have descriptions), so the schema does substantial work. The description adds no parameter-specific information beyond implying the 'prompt' parameter contains text to translate. It doesn't explain how parameters like 'images', 'dryRun', or 'ensureIPCleared' relate to translation, nor does it provide usage examples or constraints. With high schema coverage, the baseline is 3, but the description adds minimal value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool's purpose as translating text to English, which is clear but vague. It specifies the verb 'translate' and target language 'English', but doesn't mention what type of translation this is (e.g., AI-based, batch, real-time) or distinguish it from sibling tools like 'post-caption-inferences' or 'post-describe-style-inferences' that might involve language processing. The description is functional but lacks specificity about the translation mechanism or scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, limitations, or compare it to other translation-related tools in the sibling list (e.g., 'post-prompt-inferences' might handle text generation). There's no indication of when this tool is appropriate or when other tools should be used instead, leaving usage decisions entirely to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. 'Update' implies a mutation, but it doesn't disclose behavioral traits such as permission requirements, whether updates are reversible, concurrency handling (e.g., with locks), or side effects like snapshot creation. The input schema hints at lock and snapshot behaviors, but the description doesn't surface these critical aspects, leaving significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's appropriately sized and front-loaded, making it easy to parse quickly. Every word earns its place, though it could benefit from more detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (9 parameters, mutation operation) and lack of annotations and output schema, the description is incomplete. It doesn't explain what 'Update' entails, return values, error conditions, or interactions with sibling tools. For a tool with significant behavioral implications, this minimal description is inadequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is high at 89%, so the schema documents most parameters well. The description adds no parameter-specific information beyond the generic 'Update a canvas asset', which doesn't clarify semantics like what 'canvas' or 'thumbnail' entail. With high schema coverage, the baseline is 3, but the description fails to compensate for the 11% gap or add meaningful context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Update a canvas asset' clearly states the verb ('Update') and resource ('canvas asset'), which is adequate. However, it doesn't differentiate this tool from other update tools like 'put-assets-by-collection-id' or 'put-models-by-model-id', nor does it specify what aspects of the asset are updated. This makes it vague compared to siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling tools including other 'put-' operations and asset-related tools like 'post-asset' or 'lock-asset-by-asset-id', there's no indication of context, prerequisites, or exclusions. This leaves the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only states 'trigger' without clarifying behavioral traits. It doesn't disclose if this is a mutating operation (likely, given 'put'), potential side effects (e.g., resource consumption, time), authentication needs, rate limits, or expected outcomes. This leaves critical behavioral aspects undocumented.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste, front-loading the core action. It's appropriately sized for a tool name that implies its function, though it could benefit from more detail given the complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 parameters with nested objects, no output schema, no annotations), the description is inadequate. It doesn't explain the training process, expected inputs beyond 'modelId', or what 'trigger' entails, leaving significant gaps for a mutating operation with many configurable options.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50%, and the description mentions only 'modelId' without adding meaning beyond the schema. It doesn't explain the purpose of 'originalAssets', 'dryRun', or the complex 'parameters' object, failing to compensate for the low coverage. However, the schema provides detailed param descriptions, so baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Trigger the given `modelId` training' states a verb ('trigger') and resource ('training'), but is vague about what 'trigger' means (e.g., start, resume, schedule) and lacks specificity about the training process. It doesn't distinguish from siblings like 'post-model-training-action-by-model-id' or 'put-models-by-model-id', leaving ambiguity about its unique role.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., needing a configured model), exclusions, or comparisons to sibling tools such as 'post-model-training-action-by-model-id', leaving the agent with no contextual usage cues.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Unlock a canvas' implies a mutation that removes a lock, but it doesn't describe what unlocking does (e.g., makes the asset editable, releases a reservation), potential side effects, authentication needs, or error conditions. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient phrase ('Unlock a canvas') with zero waste. It's appropriately sized for a simple action, though it could benefit from more detail. Every word earns its place, making it highly concise and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given this is a mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavior, return values, error handling, and how parameters interact (e.g., forceUnlock bypassing lockId). For a tool with 4 parameters and potential side effects, more context is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all four parameters (originalAssets, assetId, lockId, forceUnlock) with clear descriptions. The tool description adds no parameter information beyond what's in the schema, not even mentioning assetId as required. Baseline 3 is appropriate when the schema does the heavy lifting, but the description doesn't compensate or add context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Unlock a canvas' states the action (unlock) and resource (canvas), providing a basic purpose. However, it's vague about what 'unlock' entails operationally and doesn't distinguish this tool from its sibling 'lock-asset-by-asset-id' beyond the opposite action. It's not tautological but lacks specificity about the unlocking mechanism or context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., needing a lockId or forceUnlock), when unlocking is appropriate, or how it relates to sibling tools like 'lock-asset-by-asset-id'. Usage is implied only by the tool name and description, with no explicit context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states the action without behavioral details. It doesn't disclose whether deletion is permanent, reversible, requires specific permissions, has rate limits, or what happens on success/failure. For a destructive operation, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at three words with zero wasted language. It's front-loaded with the core action and resource, though this brevity comes at the cost of completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a destructive tool with no annotations, 0% schema coverage, and no output schema, the description is inadequate. It doesn't explain the mutation's impact, parameter meaning, error conditions, or return values. Given the complexity and risk of asset deletion, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate but provides no parameter information. It doesn't explain what 'assetIds' represents, format expectations, or constraints. The description adds no value beyond the bare schema, leaving the single required parameter undocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Delete multiple assets' clearly states the verb (delete) and resource (assets), and specifies the scope (multiple). It distinguishes from sibling 'delete-assets-by-collection-id' by focusing on asset IDs rather than collection IDs, though it doesn't explicitly mention this distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'delete-assets-by-collection-id' or 'delete-collections-by-collection-id'. The description doesn't mention prerequisites, permissions needed, or any context for choosing between deletion methods.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only mentions the batch limit. It doesn't disclose whether this is a read-only or destructive operation, authentication requirements, rate limits, response format (e.g., link expiration), or what happens if the limit is exceeded. The description adds minimal behavioral context beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Request a link to batch download assets') and includes a key constraint ('batch limited to 1000 assets'). There is no wasted verbiage or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (2 parameters with nested objects, 0% schema coverage, no output schema, no annotations), the description is inadequate. It doesn't explain parameter usage, return values, error conditions, or behavioral traits. For a tool that likely involves significant data processing and returns a download link, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate but doesn't explain any parameters. It doesn't clarify what 'query' or 'options' contain, how to use 'modelIds', 'inferenceIds', 'assetIds', or the purpose of 'fileNameTemplate' and 'flat'. The description fails to add meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Request a link to batch download') and resource ('assets'), with a specific constraint ('batch limited to 1000 assets'). It distinguishes from siblings like 'get-assets' (which likely lists assets) and 'post-download-model' (which downloads models), but doesn't explicitly differentiate from 'get-download-assets' (which might retrieve download status).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., authentication), compare to sibling tools like 'get-download-assets' or 'post-asset-get-bulk', or specify scenarios where batch downloading is appropriate over individual downloads.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. 'Duplicate' implies a write operation that creates a copy, but it doesn't disclose critical behavioral traits: whether this requires specific permissions, if it's idempotent, what happens on failure, or if there are rate limits. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with just three words, front-loading the core action. Every word earns its place: 'Duplicate' specifies the action, 'an asset' specifies the resource. There's no wasted verbiage or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't address what the tool returns (e.g., the new asset ID, success status), error conditions, or prerequisites. Given the complexity of duplicating assets in a system with many related tools, more context is needed for the agent to use this effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so all parameters are documented in the schema. The description adds no additional meaning about parameters beyond implying duplication occurs. It doesn't clarify parameter interactions (e.g., how 'originalAssets' relates to duplication) or provide usage examples. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Duplicate an asset' clearly states the action (duplicate) and resource (asset), making the purpose immediately understandable. However, it doesn't differentiate from potential sibling operations like 'post-asset' (create new) or 'put-asset-by-asset-id' (update existing), which would require more specificity for a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling tools (e.g., 'post-asset' for creating new assets, 'put-asset-by-asset-id' for updating), there's no indication of when duplication is preferred over creation or when this tool should be avoided. The agent must infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. 'Remove assets' suggests a destructive mutation, but it doesn't disclose if this is permanent, requires specific permissions, affects other data, or has rate limits. The description adds minimal behavioral context beyond the basic action, which is insufficient for a mutation tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core action and target, making it easy to parse quickly. Every word earns its place without redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a destructive mutation tool with 2 parameters, 50% schema coverage, no annotations, and no output schema, the description is inadequate. It lacks details on behavior, error handling, return values, or how it integrates with sibling tools. The agent would struggle to use this correctly without additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50% (only 'collectionId' has a description). The description mentions 'specific collection' and 'assets', aligning with the parameters but not adding meaning beyond the schema. It doesn't explain 'assetIds' format or constraints, so it partially compensates but leaves gaps, resulting in a baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Remove assets from a specific collection' clearly states the action (remove) and target (assets from a collection). It distinguishes from siblings like 'delete-asset' (single asset) and 'delete-collections-by-collection-id' (entire collection), though not explicitly. However, it doesn't specify whether this is a bulk operation or how it differs from other deletion tools, keeping it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like 'delete-asset' (for single assets) or 'put-assets-by-collection-id' (which might update assets). The description implies usage for removing multiple assets from a collection, but lacks explicit context, prerequisites, or exclusions, leaving the agent to infer based on parameter names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Delete' implies a destructive mutation, the description fails to specify critical details such as whether deletion is permanent or reversible, what permissions are required, if there are side effects (e.g., cascading deletion of contained items), or rate limits. This leaves significant gaps in understanding the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single sentence 'Delete a collection', which is front-loaded and wastes no words. It directly states the action, making it efficient and easy to parse, though this brevity contributes to gaps in other dimensions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity as a destructive operation with no annotations and no output schema, the description is incomplete. It does not cover behavioral aspects like permanence, permissions, or effects, nor does it explain return values or error conditions. For a deletion tool, this lack of context is a significant shortfall.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'collectionId' documented as 'The collection ID to delete'. The description adds no additional parameter information, but with high schema coverage and only one parameter, the baseline is elevated. The description does not compensate for any gaps, but none exist, so it meets expectations without adding value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Delete a collection' clearly states the verb ('Delete') and resource ('a collection'), providing a basic purpose. However, it lacks specificity about what a 'collection' represents in this context (e.g., a set of assets, models, or workflows) and does not differentiate from sibling deletion tools like 'delete-assets-by-collection-id' or 'delete-models-by-collection-id', making it vague in comparison.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites (e.g., whether the collection must be empty), exclusions (e.g., cannot delete system collections), or refer to sibling tools for related operations, leaving usage entirely implicit based on the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool deletes a preset, implying a destructive mutation, but does not mention permissions, side effects, error handling, or what happens upon deletion. This is inadequate for a mutation tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence with no wasted words, making it highly concise and front-loaded. It efficiently conveys the core action without unnecessary details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a destructive tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits, success/error responses, and usage context, making it insufficient for safe and effective tool invocation by an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting both parameters. The description adds minimal value by referencing 'modelId' but does not explain parameter interactions or provide additional context beyond the schema. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Delete') and the resource ('a preset for the given modelId'), making the purpose specific and understandable. However, it does not explicitly distinguish this tool from sibling deletion tools like 'delete-models-by-model-id' or 'delete-models-training-images-by-model-id-and-training-image-id', which target different resources, so it misses full sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, such as other deletion tools in the sibling list. It lacks context about prerequisites, conditions for use, or exclusions, leaving the agent without explicit usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Remove models' implies a destructive mutation, but the description doesn't specify whether this is reversible, requires permissions, affects other data, or has side effects. For a deletion tool with zero annotation coverage, this leaves critical behavioral traits undisclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It's front-loaded with the core action and target, making it easy to parse quickly. Every word contributes directly to understanding the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a destructive mutation tool with no annotations, no output schema, and incomplete parameter documentation (50% coverage), the description is inadequate. It lacks behavioral context (e.g., permanence, errors), usage guidance relative to siblings, and details on parameters like 'modelIds'. Given the complexity and risk of deletion operations, more completeness is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50% (only 'collectionId' has a description), and the description doesn't add any parameter details beyond what's implied by the tool name. It mentions 'from a specific collection' which aligns with 'collectionId', but doesn't clarify 'modelIds' (e.g., format, constraints). With moderate schema coverage, the baseline is 3 as the description provides minimal additional value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Remove models from a specific collection' clearly states the action (remove) and target (models from a collection), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'delete-models-by-model-id' or 'delete-assets-by-collection-id', which would require specifying it's about removing multiple models from a collection rather than deleting individual models or other resource types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling deletion tools (e.g., 'delete-models-by-model-id', 'delete-assets-by-collection-id'), there's no indication of whether this is for bulk removal, specific use cases, or prerequisites. The agent must infer usage from the name and parameters alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. 'Delete a model' implies a destructive mutation, but it doesn't disclose critical behavioral traits such as whether deletion is permanent, requires specific permissions, has rate limits, or returns confirmation. This is a significant gap for a destructive operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single sentence 'Delete a model', which is front-loaded and wastes no words. It efficiently conveys the core action without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given this is a destructive mutation tool with no annotations and no output schema, the description is incomplete. It should cover behavioral aspects like permanence, permissions, or response format, but it only states the basic action, leaving critical context gaps for safe and effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the parameter 'modelId' clearly documented as 'The modelId to delete'. The description adds no additional meaning beyond this, such as format examples or constraints. Baseline 3 is appropriate since the schema handles parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Delete a model' clearly states the action (delete) and resource (model), making the purpose unambiguous. However, it doesn't differentiate from sibling tools like 'delete-models-by-collection-id' or 'delete-model-preset-by-model-id-and-preset-id', which also delete models or related entities, so it lacks sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. For example, it doesn't specify if this is for single-model deletion versus bulk deletion offered by other tools, or any prerequisites like permissions. The description offers no usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Delete' implies a destructive mutation, the description doesn't specify whether this action is reversible, requires specific permissions, has side effects (e.g., affecting model training), or what happens on success/failure. This leaves critical behavioral traits unaddressed for a destructive operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence that efficiently conveys the core action without unnecessary words. It's front-loaded with the verb 'Delete' and specifies the target precisely, making it easy to parse and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a destructive tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., idempotency, error handling), usage context, and expected outcomes, which are crucial for safe and effective tool invocation by an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with both parameters clearly documented in the input schema. The description adds no additional semantic context beyond what's in the schema (e.g., format examples, validation rules, or relationships between parameters), so it meets the baseline for high schema coverage without compensating value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Delete') and the target ('the given trainingImageId from the given modelId'), making the purpose specific and understandable. However, it doesn't explicitly differentiate this tool from sibling tools like 'delete-models-images-by-model-id' or 'delete-models-by-model-id', which appear to operate on similar resources but with different scopes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, such as whether the model or training image must exist, or compare it to sibling tools like 'delete-models-images-by-model-id' (which might delete multiple images) or 'put-models-training-images-by-model-id-and-training-image-id' (which might update instead).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions listing assets but doesn't disclose behavioral traits like pagination (implied by 'paginationToken' parameter), rate limits, authentication requirements, or whether it's read-only (implied by 'get' but not stated). The description adds minimal context beyond the tool name.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core purpose and includes two key scopes. Every word earns its place without redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (19 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the tool's behavior (e.g., pagination, default sorting), return format, error conditions, or how parameters like 'privacy' and 'collectionId' affect results. For a flexible listing tool with many filters, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 89% (high), so the baseline is 3. The description adds no parameter-specific information beyond what's in the schema—it doesn't explain how parameters interact (e.g., 'privacy' affecting scope) or provide examples. The schema already documents most parameters well, so the description doesn't compensate but doesn't need to heavily.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('assets'), specifying two scopes: 'of a project team' and 'all public assets'. It distinguishes from some siblings like 'get-assets-by-asset-id' (single asset) and 'get-public-assets' (public-only), but doesn't explicitly differentiate from 'post-search-assets' or other listing tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. The description mentions two scopes but doesn't clarify prerequisites (e.g., team membership for project assets) or compare to siblings like 'get-public-assets' (which might overlap) or 'post-search-assets' (which might offer different filtering).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'Get the details of an asset,' which implies a read-only operation, but does not cover aspects like authentication requirements, rate limits, error handling, or what 'details' include (e.g., metadata, file info). This leaves significant gaps for a tool with no annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence: 'Get the details of an asset.' It is front-loaded with the core action and resource, with no unnecessary words or fluff, making it highly efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It does not explain what 'details' entail in the response, how errors are handled, or any behavioral traits like side effects. For a tool that retrieves asset data, more context on the return format and operational constraints is needed to be fully helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting all three parameters (assetId, withEmbedding, originalAssets). The description does not add any additional meaning or context beyond what the schema provides, such as explaining the implications of including embeddings or returning original assets. Thus, it meets the baseline for high schema coverage but offers no extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get the details of an asset' clearly states the verb ('Get') and resource ('asset'), making the purpose understandable. However, it does not distinguish this tool from its sibling 'get-assets' (which likely lists multiple assets) or 'get-public-assets-by-asset-id' (which might retrieve public assets), leaving room for ambiguity in sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. For example, it does not mention when to choose this over 'get-assets' (for a single asset vs. multiple) or 'get-public-assets-by-asset-id' (for public vs. private assets), nor does it specify any prerequisites or exclusions, leaving usage context unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states this is a list operation, implying read-only behavior, but doesn't disclose any behavioral traits such as pagination handling (implied by 'paginationToken' in schema but not explained), rate limits, authentication needs, or what the snapshots contain. This leaves significant gaps for an agent to understand how to use it effectively.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It front-loads the key information ('List snapshots') and specifies the resource type clearly, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what snapshots are, their format, or how pagination works (implied by parameters but not described). For a tool with no structured behavioral data, this leaves the agent with insufficient context to use it correctly beyond basic parameter passing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so parameters are well-documented in the schema. The description doesn't add any meaning beyond the schema, such as explaining what 'snapshots' entail or how 'assetId' relates to canvas assets. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List snapshots') and resource ('of a canvas type asset'), making the tool's purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get-assets-by-asset-id' or 'get-assets', which might also retrieve asset-related data, leaving some ambiguity about when to choose this specific tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling tools like 'get-assets-by-asset-id' that might retrieve asset details, there's no indication of when snapshots are needed over other asset data, nor any prerequisites or exclusions mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states 'List collections' but doesn't disclose behavioral traits such as pagination behavior (implied by parameters but not described), rate limits, authentication needs, or what 'team' refers to. The description is minimal and lacks essential context for a read operation with parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It is front-loaded and directly conveys the core purpose without unnecessary elaboration. Every word earns its place, making it highly concise and well-structured for its simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a read operation with parameters, the description is incomplete. It lacks context on authentication, team scope, pagination behavior, and response format. For a tool with parameters and no structured support, the description should provide more guidance to be fully helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear documentation for 'pageSize' and 'paginationToken'. The description adds no parameter-specific information beyond what the schema provides. With high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract from the well-documented parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'List collections of a team' clearly states the action (list) and resource (collections), with 'of a team' providing scope. It distinguishes from siblings like 'get-collections-by-collection-id' (specific collection) and 'post-collection' (create), but doesn't explicitly contrast with all list-type tools. The purpose is specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives is provided. The description doesn't mention prerequisites (e.g., team context), exclusions, or comparisons to similar tools like 'get-collections-by-collection-id' or other list operations. Usage is implied by the name alone, with no explicit context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions retrieving 'status and url', implying a read-only operation, but doesn't disclose behavioral traits such as error handling, rate limits, authentication needs, or whether it's idempotent. This leaves significant gaps for a tool that likely interacts with asynchronous batch processes.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that efficiently conveys the core purpose without unnecessary words. It's front-loaded with the main action, though it could be slightly more structured by explicitly separating status and URL retrieval aspects.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a single parameter, the description is incomplete. It doesn't explain what the status values mean, the format of the URL, error conditions, or how this tool integrates with siblings like 'post-download-assets'. For a tool dealing with batch operations, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the single parameter 'jobId' documented as 'The job ID to retrieve the download request'. The description adds no additional meaning beyond this, such as format examples or source context. Baseline 3 is appropriate since the schema fully covers the parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Retrieve') and the target ('status and the url of a batch download assets request'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get-assets' or 'post-download-assets', which handle different aspects of asset management.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a job ID from 'post-download-assets'), exclusions, or how it fits into the workflow with other tools like 'get-job-id' or 'get-jobs'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions that jobs include 'synchronous operation or an asynchronous task' and offers 'a unified view of all operations,' which adds some context about what a job represents. However, it lacks critical behavioral details: it doesn't specify if this is a read-only operation (implied by 'List' but not explicit), whether it requires authentication, what the return format looks like (e.g., pagination behavior beyond the schema's 'paginationToken'), or any rate limits. For a tool with 7 parameters and no annotations, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with two sentences. The first sentence front-loads the core purpose ('List all jobs matching the given filters'), and the second adds useful context about what a job is. There's no wasted language, though it could be slightly more structured (e.g., separating usage notes).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (7 parameters, no annotations, no output schema), the description is incomplete. It explains what a job is but doesn't cover behavioral aspects like safety (read-only vs. mutation), authentication needs, response format, or error handling. Without annotations or an output schema, the description should do more to compensate, such as detailing the return structure or usage constraints.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 7 parameters thoroughly. The description adds minimal value beyond the schema: it mentions 'filters' generically, which aligns with parameters like 'authorId', 'type', and 'status', but doesn't provide additional syntax, format details, or examples. With high schema coverage, the baseline is 3, and the description doesn't significantly enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'List all jobs matching the given filters' with a specific verb ('List') and resource ('jobs'). It distinguishes from siblings by focusing on jobs rather than assets, models, or workflows. However, it doesn't explicitly differentiate from other job-related tools like 'get-job-id' beyond the listing vs. single retrieval distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get-job-id' (for retrieving a single job) or explain when filtering is needed versus other job-related operations. The context is implied through the mention of filters, but no explicit usage scenarios or exclusions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states this is a list operation, implying it's read-only and non-destructive, but doesn't mention any behavioral traits like pagination, rate limits, authentication requirements, or what happens if the modelId is invalid. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that directly states the tool's purpose without any unnecessary words. It's front-loaded and efficiently communicates the core functionality, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is insufficient for a retrieval tool. It doesn't explain what presets are, what data they contain, or the format of the returned list. With no structured data to compensate, the description should provide more context about the output and usage scenarios to be complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the input schema already fully documents both parameters (modelId and originalAssets). The description mentions 'modelId' but adds no additional semantic context beyond what's in the schema, such as format examples or how originalAssets affects the output. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List all presets') and the target resource ('for the given modelId'), making the purpose immediately understandable. However, it doesn't differentiate this tool from similar sibling tools like 'get-models-by-model-id' or 'get-models-description-by-model-id', which also retrieve model-related data but for different resources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling tools available (e.g., 'get-models-by-model-id', 'get-models-description-by-model-id'), there's no indication of when presets specifically are needed or what distinguishes this retrieval from other model-related queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but provides minimal behavioral insight. It mentions the tool retrieves details including training status/progress 'if available', hinting at conditional data, but lacks crucial context like authentication needs, rate limits, error handling, or response format. This is inadequate for a read operation with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose. It avoids redundancy and wastes no words, though it could be slightly more structured by separating purpose from conditional details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read tool with no annotations and no output schema, the description is incomplete. It lacks information on response format (e.g., JSON structure), error cases (e.g., invalid modelId), and operational constraints (e.g., access permissions). The mention of 'if available' for training data is helpful but insufficient for full context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents both parameters. The description adds no parameter-specific information beyond implying 'modelId' is required (already in schema). It doesn't explain the purpose of 'originalAssets' or provide examples, so baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('details of the given modelId'), specifying what information is retrieved (training status and progress). It distinguishes from siblings like 'get-models' (list) and 'get-models-description-by-model-id' (specific field), but doesn't explicitly mention these alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives is provided. The description implies it's for retrieving details of a specific model, but doesn't compare it to siblings like 'get-models' (list all) or 'get-models-description-by-model-id' (partial info), leaving usage context unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states a read operation ('Get'), which implies it's likely safe and non-destructive, but doesn't confirm this or add any other behavioral traits. It lacks details on permissions, rate limits, error handling, or the format of the returned scores (e.g., numeric values, timestamps), which are crucial for a tool with unspecified output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—a single sentence that directly states the tool's purpose without any unnecessary words. It's front-loaded and efficient, making it easy to parse quickly, though this brevity contributes to gaps in other dimensions like guidelines and transparency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a tool with 2 parameters (one undocumented), no annotations, and no output schema, the description is incomplete. It doesn't explain what 'prompt scores' entail (e.g., evaluation metrics, user ratings), how results are structured, or any behavioral constraints. For a data retrieval tool in a context-rich environment with many siblings, more detail is needed to ensure proper usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions the 'modelId' parameter, aligning with the input schema's required parameter, but doesn't explain the optional 'prompt' parameter, leaving its purpose unclear. With schema description coverage at 50% (only 'modelId' has a description), the description adds minimal value beyond the schema—it clarifies that 'modelId' is for retrieving prompt scores, but fails to compensate for the undocumented 'prompt' parameter, resulting in a baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and the target resource ('prompt scores for the given modelId'), which is specific and understandable. However, it doesn't explicitly differentiate this tool from similar sibling tools like 'get-models-description-by-model-id' or 'get-models-examples-by-model-id', which also retrieve model-specific data, leaving some ambiguity about what distinguishes prompt scores from other model attributes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., whether the model must exist or be accessible), exclusions, or how it differs from other 'get-models-*' tools in the sibling list, such as 'get-models-scores-training-dataset-by-model-id'. This lack of context makes it harder for an AI agent to choose appropriately among similar tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It implies a read-only operation ('Get'), but doesn't specify whether this requires authentication, has rate limits, returns paginated results, or what the output format might be. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior and constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse quickly, with no wasted verbiage or structural issues.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema, the description is insufficiently complete. It lacks details on behavioral traits (e.g., authentication needs, rate limits), output format, and differentiation from siblings. While concise, it doesn't provide enough context for an agent to fully understand how to use this tool effectively in a complex environment with many similar tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'modelId' documented as 'The training dataset scores' modelId'. The description adds no additional semantic context beyond this, such as explaining what a 'modelId' is or how to obtain it. Given the high schema coverage, a baseline score of 3 is appropriate, as the description doesn't compensate but doesn't detract either.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and resource ('training dataset scores') with a specific identifier ('for the given modelId'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'get-models-scores-prompt-by-model-id' or 'get-models-by-model-id', which also retrieve model-related data, leaving some ambiguity about what exactly 'scores' refers to in this context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, such as other 'get-models-*' siblings that might retrieve different aspects of model data. It lacks context about prerequisites, typical use cases, or exclusions, offering only a basic functional statement without strategic direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. 'List all public models' implies a read-only operation, but it doesn't mention pagination behavior, rate limits, authentication requirements, or what 'public' entails (e.g., visibility constraints). For a tool with 12 parameters and no annotation coverage, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence ('List all public models') that is front-loaded and wastes no words. It directly communicates the core action without unnecessary elaboration, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (12 parameters, no annotations, no output schema), the description is inadequate. It doesn't explain the return format, pagination, filtering logic, or how it differs from sibling tools. For a list operation with extensive filtering options, more context is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with detailed parameter documentation in the input schema. The description adds no parameter semantics beyond implying a listing action. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract from the well-documented parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'List all public models' clearly states the verb ('List') and resource ('public models'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'get-models' or 'get-public-models-by-model-id', which would require specifying that this tool retrieves all public models rather than filtering by ID or other criteria.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'get-models' and 'get-public-models-by-model-id' available, the agent has no indication whether this tool is for bulk retrieval, filtered queries, or specific use cases, leaving usage decisions ambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states it's a listing operation. It doesn't disclose behavioral traits like whether this is a read-only operation (implied but not stated), pagination behavior (only hinted via 'nextToken' in schema), rate limits, authentication requirements, or what 'recommended' means algorithmically. The description adds minimal value beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose. Every word earns its place with no redundancy or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 9 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what 'recommended' means, how results are ordered, what the output format looks like, or provide any context about the recommendation algorithm. The description leaves too many questions unanswered for a tool of this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters are fully documented in the schema. The description adds no additional parameter semantics beyond implying filtering occurs. Baseline 3 is appropriate since the schema does all the parameter documentation work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'List recommended models matching the given filters' clearly states the action (list) and resource (recommended models) with a qualifier (matching filters). It distinguishes from generic 'get-models' siblings by specifying 'recommended' models, though it doesn't explicitly contrast with 'get-public-models' or 'post-search-models' which might have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'get-models', 'get-public-models', or 'post-search-models'. It mentions 'recommended' but doesn't explain what makes a model recommended or when this tool is preferable over other listing/search tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool lists tags but fails to mention key traits like whether it's read-only (implied by 'List'), pagination behavior (hinted by parameters but not explained), rate limits, or authentication needs. This leaves significant gaps in understanding the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It is front-loaded and wastes no space, making it highly concise and well-structured for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It does not address behavioral aspects like pagination, error handling, or return format, which are critical for a list operation with pagination parameters. This leaves the agent with insufficient context to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the input schema fully documents the two parameters (pageSize and paginationToken). The description adds no additional meaning beyond the schema, as it does not explain parameter usage or relationships. The baseline score of 3 is appropriate when the schema handles parameter documentation effectively.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List all tags') and the resource ('in use for the given projectId'), providing a specific verb+resource combination. However, it does not explicitly differentiate from sibling tools like 'get-assets' or 'get-models', which operate on different resources, so it misses the highest score for sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It lacks any mention of prerequisites, such as needing a valid projectId, or comparisons to other tag-related tools (e.g., 'put-models-tags-by-model-id'), leaving the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. 'Upload' implies a write operation, but the description doesn't mention authentication requirements, rate limits, whether the upload is public/private, what happens on success/failure, or any side effects. For a mutation tool with 8 parameters, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise at just three words. Every word earns its place: 'Upload' specifies the action, 'image or canvas' specifies the resource types. There's no wasted text or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with 8 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what happens after upload (e.g., returns an asset ID), doesn't mention authentication or permissions, and doesn't clarify the relationship between image/canvas parameters. The agent lacks critical context for proper tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 88%, so the schema already documents most parameters well. The description mentions 'image or canvas' which hints at the 'image' and 'canvas' parameters, but doesn't add meaningful context beyond what the schema provides. With high schema coverage, the baseline of 3 is appropriate as the description adds minimal value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Upload an image or canvas' clearly states the verb (upload) and resource (image/canvas), making the tool's purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'post-uploads' or 'post-asset-get-bulk' that might also handle uploads, so it doesn't reach the highest clarity level.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling tools including 'post-uploads' and various inference tools, there's no indication of whether this is for general asset uploads, specific formats, or particular workflows. The agent must infer usage from the name and parameters alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states a read operation ('Get'), implying non-destructive behavior, but doesn't disclose critical details like authentication needs, rate limits, error handling, or response format. For a bulk retrieval tool with zero annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It front-loads the core action ('Get multiple assets') and specifies the key constraint ('by their IDs'). Every word earns its place, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a bulk retrieval tool with 2 parameters, 50% schema coverage, no annotations, and no output schema, the description is inadequate. It lacks details on authentication, response format, error cases, and parameter usage. Given the complexity and missing structured data, it should provide more context to be complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50% (only 'originalAssets' has a description). The description mentions 'by their IDs', which aligns with the 'assetIds' parameter but adds no syntax or format details. It doesn't explain 'originalAssets' or compensate for the undocumented parameter. With partial schema coverage, the description provides minimal added value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('multiple assets'), specifying retrieval by IDs. It distinguishes from siblings like 'get-assets' (likely unfiltered list) and 'get-assets-by-asset-id' (single asset), but doesn't explicitly name alternatives. The purpose is specific but lacks explicit sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives is provided. The description implies bulk retrieval by IDs, but doesn't specify prerequisites, limitations (e.g., max IDs), or compare to siblings like 'get-assets' or 'post-search-assets'. Usage is implied by the name and description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. 'Create' implies a write operation, but it doesn't disclose behavioral traits such as permissions needed, whether it's idempotent, rate limits, or what happens on failure. This is inadequate for a mutation tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It's front-loaded and directly states the action, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavior, error handling, or return values, leaving significant gaps for an AI agent to understand how to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'name' parameter fully documented. The description adds no additional meaning beyond the schema, such as constraints on the name format or examples. Baseline 3 is appropriate since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Create' and the resource 'collection', making the purpose unambiguous. However, it doesn't differentiate from sibling tools like 'post-asset' or 'post-models' that also create resources, so it's not fully specific to collections.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, related tools like 'get-collections' for listing, or when to choose other creation tools like 'post-asset' for assets.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the tool 'triggers a new image generation' which implies a write/mutation operation, but doesn't disclose important behavioral traits like whether this is an asynchronous operation, what permissions are required, rate limits, or what happens to the generated images. The description is too brief to adequately cover behavioral aspects for a complex image generation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - just two sentences that directly state the tool's purpose. It's front-loaded with the core functionality and wastes no words. Every sentence earns its place by explaining what the tool does and how the control image functions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex tool with 28 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns, how to handle the generated images, error conditions, or important behavioral aspects. The high parameter count and complexity demand more comprehensive guidance that the brief description doesn't provide.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 93%, so the schema already documents most parameters thoroughly. The description adds minimal value beyond the schema - it mentions the control image purpose but doesn't explain parameter relationships or provide additional context that isn't already in the parameter descriptions. With high schema coverage, a baseline of 3 is appropriate as the description doesn't significantly enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Trigger a new image generation in ControlNet mode' with the control image guiding generation. It specifies the resource (image generation) and verb (trigger), and distinguishes it from other image generation tools by mentioning ControlNet mode. However, it doesn't explicitly differentiate from sibling ControlNet tools like 'post-controlnet-img2img-inferences' or 'post-controlnet-inpaint-inferences'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal usage guidance. It mentions that the control image 'can be a pose, canny map, or similar' which gives some context about when to use ControlNet, but doesn't specify when to choose this tool over other ControlNet variants (img2img, inpaint, etc.) or over regular image generation tools. No explicit alternatives, prerequisites, or exclusions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool triggers image generation, implying a write operation, but fails to mention critical behavioral aspects such as permissions required, rate limits, whether it's asynchronous, what the output looks like, or error handling. For a complex tool with 32 parameters, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, consisting of two sentences that directly state the tool's purpose and key components. Every sentence earns its place by explaining the core functionality without unnecessary details. However, it could be slightly more structured by explicitly mentioning it's for AI image generation, but overall it's efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (32 parameters, no annotations, no output schema), the description is inadequate. It lacks information on behavioral traits, output format, error conditions, and usage context. While the schema covers parameters well, the description does not compensate for the missing annotations and output details, making it incomplete for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is high at 94%, so the schema already documents most parameters thoroughly. The description adds minimal value beyond the schema by briefly explaining the control image and mask roles, but it does not provide additional context, examples, or clarifications for key parameters like 'modality' or 'modelId'. Baseline 3 is appropriate given the schema's heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Trigger a new image generation in ControlNet + Inpaint mode.' It specifies the verb ('trigger'), resource ('image generation'), and mode ('ControlNet + Inpaint'), and explains the roles of control image and mask. However, it does not explicitly differentiate from sibling tools like 'post-controlnet-inferences' or 'post-inpaint-inferences', which is why it doesn't reach a score of 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions the control image and mask functions but does not specify scenarios, prerequisites, or exclusions compared to other image generation tools in the sibling list. This lack of comparative context leaves the agent without clear usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the functional role of inputs (control image guides, mask indicates inpainting area, reference image guides via IpAdapter) but lacks critical behavioral details: it doesn't mention that this is a generative/mutation operation (implied by 'trigger'), doesn't specify permissions, rate limits, or what happens to existing assets. For a complex tool with 35 parameters, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise (two sentences) and front-loaded with the core purpose. Every sentence adds value: the first states the action and mode, the second explains the three key input roles. There's no wasted verbiage, though it could be slightly more structured (e.g., bullet points for input roles).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (35 parameters, no annotations, no output schema), the description is inadequate. It doesn't cover behavioral aspects like mutation effects, error conditions, or output format. While the schema handles parameter details, the description fails to provide the holistic context needed for safe and effective use, especially for a generative tool with potential side effects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is high (86%), so the schema already documents most parameters well. The description adds minimal value beyond the schema: it explains the roles of control image, mask, and reference image, but doesn't clarify parameter interactions or provide examples beyond what's in schema descriptions. With high coverage, the baseline is 3, and the description doesn't significantly enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Trigger a new image generation in ControlNet + Inpaint + IpAdapter mode.' It specifies the verb ('trigger'), resource ('image generation'), and mode, distinguishing it from simpler image generation tools. However, it doesn't explicitly differentiate from similar sibling tools like 'post-controlnet-inpaint-inferences' or 'post-controlnet-ip-adapter-inferences', which would require more specific comparison.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions the components (ControlNet, Inpaint, IpAdapter) but doesn't explain when this combined mode is preferable over using separate tools or other sibling tools like 'post-controlnet-inpaint-inferences'. Without such context, an agent must infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. The description mentions that it 'triggers a new image generation,' implying a write operation, but does not disclose other behavioral traits such as whether it requires specific permissions, rate limits, or what the response looks like (e.g., returns generated images). For a complex tool with 35 parameters and no annotations, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, consisting of two sentences that directly state the tool's purpose. Every sentence earns its place by explaining the core functionality without unnecessary details. However, it could be slightly more structured by explicitly naming key parameters or usage scenarios.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (35 parameters, no output schema, no annotations), the description is incomplete. It lacks information on behavioral aspects, output format, and detailed usage context. While the schema covers many parameters, the description does not adequately address the tool's overall behavior or integration with sibling tools, making it insufficient for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is high at 86%, so the schema already documents most parameters well. The description adds minimal parameter semantics by mentioning 'control image' and 'second reference image,' which loosely map to parameters like 'controlImage' and 'ipAdapterImages,' but does not provide additional details beyond what the schema offers. With high coverage, the baseline is 3, and the description does not significantly compensate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Trigger a new image generation in ControlNet + IpAdapter mode.' It specifies the verb ('trigger'), resource ('image generation'), and mode ('ControlNet + IpAdapter'), making the action explicit. However, it does not distinguish this tool from sibling tools like 'post-controlnet-inferences' or 'post-controlnet-img2img-inferences', which limits differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal usage guidance. It mentions that 'The control image is used to guide the generation; it can be a pose, canny map, or similar' and 'The second reference image is used as an IPAdapter to guide the generation process,' which implies context but does not explicitly state when to use this tool versus alternatives like 'post-controlnet-inferences' or 'post-img2img-ip-adapter-inferences.' No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool 'triggers' a generation, implying a write operation, but doesn't describe side effects (e.g., resource consumption, rate limits), permissions required, or what happens on failure. The description adds some context about control images but lacks critical behavioral details for a complex generative tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, consisting of two clear sentences that directly state the tool's purpose and a key aspect of its usage. There is no unnecessary information, and every sentence contributes to understanding the tool's function efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (28 parameters, no output schema, no annotations), the description is inadequate. It lacks information on behavioral traits, output format, error handling, and usage distinctions from siblings. For a generative AI tool with many parameters and no structured safety hints, the description should provide more context to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description does not mention any parameters, leaving all semantic details to the input schema. With a high schema description coverage of 93%, the schema provides comprehensive parameter documentation. The description adds no value beyond the schema, so it meets the baseline of 3 for high coverage but doesn't compensate with additional insights.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Trigger a new seamless texture image generation in ControlNet mode.' It specifies the action (trigger generation), the resource (seamless texture image), and the mode (ControlNet). However, it doesn't explicitly differentiate from sibling tools like 'post-controlnet-inferences' or 'post-texture-inferences', which reduces clarity about its unique role.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal usage guidance. It mentions that 'The control image is used to guide the generation; it can be a pose, canny map, or similar,' which gives some context but doesn't specify when to use this tool versus alternatives (e.g., other ControlNet or texture-related tools in the sibling list). No explicit when-not-to-use or alternative recommendations are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool requests a download link but doesn't describe what happens next (e.g., whether the link is temporary, requires authentication, has rate limits, or what format the response takes). This leaves significant gaps for a tool that likely involves network operations and resource access.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose with zero wasted words. It's appropriately sized for a simple operation and front-loaded with the core action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations, no output schema, and incomplete parameter documentation (50% schema coverage), the description is inadequate. It doesn't explain the behavioral aspects, response format, or usage context needed for an agent to effectively invoke this tool. The conciseness comes at the cost of completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50% (only 'modelEpoch' has a description). The description mentions 'modelId' but adds no details beyond what's implied by the parameter name. It doesn't explain the optional 'modelEpoch' parameter or provide context for when it's needed. The description adds minimal value over the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Request a link to download') and the resource ('the given modelId'), making the purpose specific and understandable. However, it doesn't differentiate from sibling tools like 'post-download-assets' or 'get-download-assets', which handle similar download operations for different resource types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, when this tool is appropriate compared to other download-related tools in the sibling list, or any constraints on its use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions the tool is for 'erase objects or characters,' implying a destructive/mutative operation, but lacks details on permissions, rate limits, output format, or error handling. For a complex tool with 12 parameters and no annotations, this is insufficient behavioral disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief and front-loaded with the core purpose in the first sentence. The second sentence adds a use case without redundancy. However, it could be more structured by explicitly stating inputs/outputs or behavioral traits, but it avoids unnecessary verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (12 parameters, no output schema, no annotations), the description is inadequate. It doesn't explain the return value, error conditions, or how the generative fill interacts with parameters like prompt and negativePrompt. For a tool with rich input schema but no other structured data, more context is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is high at 92%, so the schema already documents most parameters well. The description adds no specific parameter information beyond implying mask-based operations. It doesn't clarify parameter interactions (e.g., mask vs. maskId) or provide examples beyond the generic use case, meeting the baseline but not adding significant value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generative fill replace the selected mask area content based on the context.' It specifies the action (replace), target (mask area), and method (generative fill based on context). However, it doesn't explicitly differentiate from sibling tools like 'post-inpaint-inferences' or 'post-patch-inferences' that might have similar image editing functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal usage guidance with 'Used to erase objects or characters,' which gives a specific use case but doesn't explain when to choose this tool over alternatives. With many sibling tools for image manipulation (e.g., post-inpaint-inferences, post-restyle-inferences), there's no explicit comparison or context for selection, leaving the agent with little direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'triggers' a generation, implying a write operation that may consume resources, but doesn't mention costs, rate limits, permissions, or what happens to the reference image. For a complex generative AI tool with 23 parameters, this lack of behavioral context is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose. It wastes no words and directly states what the tool does, making it easy for an agent to parse quickly. Every word earns its place, with no redundant or vague phrasing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (23 parameters, no annotations, no output schema), the description is inadequate. It doesn't explain the output format, error conditions, or how results are returned. For a generative AI tool that likely produces images or asset IDs, the lack of output information is a major omission, leaving the agent unprepared for what happens after invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is high at 91%, so the schema already documents most parameters thoroughly. The description adds minimal value beyond the schema, only implying that 'one reference image' is required (mapping to the 'image' parameter) without explaining parameter interactions or dependencies. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate for the remaining 9% gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Trigger a new image generation in Img2Img mode with one reference image that initializes the generation process.' It specifies the verb ('trigger'), resource ('image generation'), and mode ('Img2Img'), distinguishing it from other inference tools like txt2img or controlnet. However, it doesn't explicitly differentiate from similar siblings like 'post-controlnet-img2img-inferences' or 'post-img2img-ip-adapter-inferences' beyond the basic mode.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions 'Img2Img mode' but doesn't explain when this mode is appropriate compared to txt2img, controlnet, or other variants. There are no prerequisites, exclusions, or named alternatives provided, leaving the agent to infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool triggers a 'new image generation', implying a write/mutation operation, but doesn't clarify permissions, rate limits, costs, or what happens to input images. It mentions the two-image process but lacks details on output format, error handling, or performance characteristics. For a complex 32-parameter mutation tool, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, consisting of two clear sentences that immediately convey the core functionality. Every sentence earns its place: the first states the action and mode, the second explains the two-image roles. There's no redundant or verbose phrasing, making it efficient for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (32 parameters, mutation operation, no annotations, no output schema), the description is incomplete. It doesn't address critical context like what the tool returns (e.g., generated images, job IDs), error conditions, or how it fits into a broader workflow. For a sophisticated image generation tool with many parameters, more guidance on typical use cases and output expectations is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is high at 84%, establishing a baseline of 3. The description adds minimal parameter semantics beyond the schema, only clarifying that the first image initializes generation and the second serves as an IPAdapter reference. It doesn't explain how parameters like 'strength' or 'ipAdapterScale' interact in this specific mode, nor does it provide examples or contextual tips for parameter selection.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Trigger a new image generation in Img2Img + IpAdapter mode.' It specifies the verb ('trigger'), resource ('image generation'), and mode ('Img2Img + IpAdapter'), distinguishing it from generic image generation tools. However, it doesn't explicitly differentiate from similar siblings like 'post-img2img-inferences' or 'post-txt2img-ip-adapter-inferences' beyond mentioning the mode.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal usage guidance. It explains the role of the two images (first for initialization, second as IPAdapter reference) but offers no explicit advice on when to use this tool versus alternatives like 'post-img2img-inferences' or 'post-controlnet-ip-adapter-inferences'. There's no mention of prerequisites, constraints, or typical scenarios for choosing this specific mode.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions triggering a 'new seamless texture image generation,' implying a write operation that likely consumes resources, but does not address critical aspects like authentication requirements, rate limits, cost implications, or whether the operation is asynchronous. For a complex tool with 25 parameters and no annotations, this is insufficient, warranting a score of 2.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary details. It is front-loaded and wastes no words, making it easy for an agent to quickly grasp the core functionality. This exemplifies conciseness, earning a score of 5.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (25 parameters, no annotations, no output schema), the description is inadequate. It lacks information on behavioral traits, output format, error handling, and usage context. While the schema covers most parameters, the description fails to provide the necessary contextual completeness for a tool of this nature, resulting in a score of 2.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds minimal parameter semantics beyond the input schema, which has high coverage (92%). It implies the use of a 'reference image' (mapping to the 'image' parameter) but does not explain key parameters like 'prompt' or 'modelId' beyond what the schema provides. Given the high schema coverage, the baseline is 3, as the description does not significantly enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Trigger a new seamless texture image generation in Img2Img mode with one reference image that initializes the generation.' It specifies the action (trigger generation), resource (seamless texture image), and mode (Img2Img). However, it does not explicitly differentiate from sibling tools like 'post-texture-inferences' or 'post-txt2img-texture-inferences', which limits the score to 4.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention any prerequisites, exclusions, or comparisons to sibling tools such as 'post-texture-inferences' or 'post-img2img-inferences', leaving the agent without context for selection. This lack of usage guidance results in a score of 2.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the tool triggers a cancel action, implying mutation/destructive behavior, but doesn't disclose important traits: whether cancellation is reversible, permission requirements, rate limits, or what the response contains. For a mutation tool with zero annotation coverage, this is inadequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise with zero wasted words. The single sentence 'Trigger an action on a job: cancel' is front-loaded and efficiently communicates the core purpose. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't explain behavioral consequences (what cancellation entails), success/failure responses, or constraints (only inference jobs). Given the complexity of job management and rich sibling toolset, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents both parameters (jobId, action). The description adds no parameter-specific information beyond what's in the schema. The baseline score of 3 reflects adequate coverage by the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('trigger') and resource ('job'), specifying the action is 'cancel'. It distinguishes from siblings by focusing on job actions rather than asset/model operations. However, it doesn't explicitly differentiate from similar action tools like 'post-model-training-action-by-model-id'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives is provided. The description doesn't mention prerequisites (e.g., job must be running), limitations (only inference jobs), or what happens after cancellation. With many sibling tools, this lack of context is a significant gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states 'Create a new model', implying a write operation, but lacks details on permissions, side effects, rate limits, or response format. This is inadequate for a mutation tool with complex parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's appropriately sized and front-loaded, making it easy to parse quickly without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with 8 parameters, no annotations, and no output schema, the description is incomplete. It fails to address behavioral aspects like what happens post-creation, error conditions, or how it integrates with other tools, leaving significant gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 75%, providing good parameter documentation. The description adds no additional meaning beyond the schema, such as explaining relationships between parameters like 'concepts' and 'baseModelId'. Baseline 3 is appropriate as the schema does most of the work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Create') and resource ('a new model'), making the tool's purpose evident. However, it doesn't differentiate from sibling tools like 'post-models-copy-by-model-id' or 'post-models-transfer-by-model-id', which also involve model creation or manipulation, so it misses full sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'post-models-copy-by-model-id' for copying models, there's no indication of prerequisites, context, or exclusions for creating a new model from scratch.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions copying training data if present, which adds some behavioral context, but fails to disclose critical traits: whether this is a read-only or mutating operation (implied mutation from 'copy'), authentication requirements, rate limits, or what the output looks like (no output schema). For a tool with potential side effects, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core action. It avoids redundancy but could be slightly more structured (e.g., clarifying parameter dependencies). Every word earns its place, making it appropriately concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (copying a model with training data), lack of annotations, and no output schema, the description is incomplete. It doesn't address behavioral risks (e.g., whether copying affects the original), response format, or error conditions. For a tool with multiple parameters and potential side effects, this leaves significant gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all four parameters. The description adds minimal value beyond the schema—it implies copying of training images and pairs relates to 'copyExamples' and 'copyAsTrained', but doesn't explain parameter interactions or defaults. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Copy') and the resource ('modelId'), specifying what gets copied (thumbnail, presets, training images and pairs). It distinguishes from sibling tools like 'post-models-transfer-by-model-id' by focusing on duplication rather than transfer. However, it doesn't explicitly differentiate from 'copy-asset-by-asset-id' in terms of scope (model vs. asset).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'post-models-transfer-by-model-id' or 'copy-asset-by-asset-id'. It lacks context about prerequisites (e.g., needing an existing modelId) or typical use cases (e.g., creating a backup or variant).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states a read operation ('Get'), implying it's non-destructive, but doesn't disclose behavioral traits like authentication requirements, rate limits, error handling, or response format. The description is minimal and lacks essential context for safe and effective use.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose. It's front-loaded with the core action and resource, with zero wasted words. Every part of the sentence earns its place by specifying the operation and key input.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (7 parameters, no annotations, no output schema), the description is insufficient. It doesn't explain the tool's role in the system, how it differs from siblings, what it returns, or any behavioral constraints. For a bulk retrieval tool with multiple configuration options, more context is needed to guide proper use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is high (86%), so the baseline is 3. The description mentions 'modelIds' as the key parameter, aligning with the schema, but adds no additional meaning beyond what's documented in the schema properties. It doesn't explain parameter interactions or provide usage examples.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and resource ('multiple models'), specifying the key identifier ('modelIds'). It distinguishes from individual model retrieval tools like 'get-models-by-model-id' by emphasizing bulk retrieval. However, it doesn't explicitly differentiate from other bulk operations like 'post-asset-get-bulk' or list operations like 'get-models'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose this over 'get-models' (which likely lists all models) or 'get-models-by-model-id' (for single models), nor does it specify prerequisites like authentication or rate limits. Usage context is implied but not articulated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states this is an 'Add' operation, implying creation/mutation, but doesn't disclose behavioral traits like required permissions, whether it's idempotent, rate limits, or what happens on success/failure (e.g., returns a training image ID). For a mutation tool with zero annotation coverage, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without fluff. Every word earns its place, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (7 parameters, mutation operation) and lack of annotations/output schema, the description is incomplete. It doesn't cover behavioral aspects (e.g., side effects, error handling), parameter dependencies, or output expectations. For a tool that adds training images to models, more context is needed to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is high (86%), so the baseline is 3. The description adds no parameter semantics beyond the schema—it mentions 'modelId' but doesn't explain parameter interactions (e.g., 'assetId' overrides 'data' and 'name') or provide usage examples. The schema already documents parameters well, so no extra value is added.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Add a new training image') and target resource ('to the given modelId'), which is specific and unambiguous. However, it doesn't distinguish this tool from sibling tools like 'post-asset' or 'put-models-training-images-by-model-id-and-training-image-id', which appear to handle similar image-related operations for models.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., model must exist), exclusions (e.g., not for updating existing images), or refer to sibling tools like 'put-models-training-images-by-model-id-and-training-image-id' for updates or 'post-asset' for general asset uploads.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions 'transfer' implying mutation, but doesn't disclose behavioral traits such as required permissions, whether the operation is reversible, rate limits, or what happens to the original model. The phrase 'including all of its training images' adds some context but is insufficient for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads key information. It avoids redundancy and wastes no words, though it could be slightly more structured by separating copy vs. ownership scenarios.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations, no output schema, and incomplete parameter documentation, the description is inadequate. It lacks details on behavior, error conditions, return values, and usage context, leaving significant gaps for an AI agent to understand and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 67% (2 out of 3 parameters have descriptions). The description doesn't add meaning beyond the schema—it doesn't explain parameter interactions (e.g., destinationProjectId vs. destinationTeamId) or provide examples. With moderate schema coverage, the baseline of 3 is appropriate as the description doesn't compensate for gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Transfer'), the resource ('a model'), and the scope ('including all of its training images'). It distinguishes between copy and ownership change, but doesn't explicitly differentiate from sibling tools like 'post-models-copy-by-model-id' or 'put-models-by-model-id' which might have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description mentions 'copy or a full ownership change' but doesn't specify scenarios for each option or prerequisites like permissions. Sibling tools include various model-related operations, but no context is given for choosing this one.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that extra space is filled based on context, which implies generative AI behavior, but doesn't disclose critical traits like whether this is a read-only or destructive operation, authentication needs, rate limits, or output format. For a complex image transformation tool with 16 parameters, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—two sentences that directly state the tool's purpose and a key behavioral aspect. Every word earns its place with no redundancy or fluff, making it easy to parse quickly. It's appropriately sized for a tool with a clear primary function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (16 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the generative AI nature, potential side effects, error conditions, or what the output looks like. While the schema covers parameter details, the description fails to provide the broader context needed for safe and effective use of this image transformation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 88%, so the schema already documents most parameters well. The description adds minimal value beyond the schema—it implies reframing involves resizing and context-based filling, which aligns with parameters like targetWidth/targetHeight and prompt, but doesn't explain parameter interactions or provide additional semantic context. Baseline 3 is appropriate given high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Reframe a given image to new sizes' specifies the action and resource. It distinguishes from siblings by focusing on image reframing rather than other image operations like captioning, inpainting, or background removal. However, it doesn't explicitly differentiate from similar tools like 'post-restyle-inferences' or 'post-generative-fill-inferences' that might involve image modification.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal usage guidance. 'Extra space is filled based on the context' hints at when to use it, but it doesn't specify when to choose this tool over alternatives like 'post-restyle-inferences' or 'post-generative-fill-inferences'. No explicit when-not-to-use scenarios or prerequisites are mentioned, leaving the agent with little contextual direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions 'trigger a restyle process' but lacks details on behavioral traits such as whether it's a read-only or mutating operation, expected runtime, rate limits, authentication needs, or what the output entails (e.g., returns an image, job ID, or error). This is inadequate for a tool with 13 parameters and no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core action and inputs without unnecessary words. It earns its place by clearly stating the tool's purpose in a compact form, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (13 parameters, no annotations, no output schema), the description is insufficient. It lacks information on output behavior, error handling, or operational constraints, which are critical for an AI agent to invoke it correctly. The high parameter count and absence of structured behavioral data require more descriptive context than provided.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is high at 85%, so the schema already documents most parameters well. The description adds minimal value by hinting at the inputs ('sketch image (or other image)' and 'reference style images'), which loosely maps to 'image' and 'styleImages' parameters, but doesn't provide additional context beyond the schema's descriptions. Baseline 3 is appropriate given the schema's thoroughness.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('trigger a restyle process') and specifies the inputs ('from one sketch image (or other image) and one or more reference style images'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'post-controlnet-inferences' or 'post-img2img-inferences' that might also involve image transformation, leaving some ambiguity about its unique role.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling tools for image processing (e.g., 'post-controlnet-inferences', 'post-img2img-inferences'), there is no indication of specific scenarios, prerequisites, or exclusions for this restyle tool, leaving the agent to infer usage from context alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the process 'creates a new Asset,' implying a write operation, but does not specify permissions, rate limits, error handling, or the nature of the output (e.g., format, size). This leaves significant gaps in understanding the tool's behavior beyond basic functionality.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, consisting of two clear sentences that state the action and outcome without unnecessary details. It efficiently communicates the core purpose, though it could be slightly more structured by including key usage notes.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (13 parameters, no output schema, no annotations), the description is inadequate. It lacks details on output format, error conditions, performance characteristics, and how parameters affect the segmentation process. This makes it incomplete for effective agent use in a rich context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description does not add any parameter-specific information beyond what the input schema provides. With a schema description coverage of 77%, the baseline is 3, as the schema documents most parameters adequately. The description fails to compensate for the 23% gap or provide additional context on parameter interactions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Trigger the segmentation of an image') and the outcome ('create a new Asset with the segmentation mask as a child'), making the purpose specific and understandable. However, it does not explicitly differentiate from sibling tools like 'post-remove-background-inferences' or 'post-detect-inferences', which might also involve image processing, so it misses full sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, such as other image processing tools in the sibling list. It lacks context on prerequisites, use cases, or exclusions, leaving the agent without clear usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'trigger the generation' which implies a potentially long-running or asynchronous process, but fails to detail execution behavior, error handling, rate limits, authentication needs, or output format. This is inadequate for a complex tool with many parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and wastes no space, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (22 parameters, no output schema, no annotations), the description is insufficient. It lacks information on output format, execution behavior, error cases, and practical usage context. For a generative AI tool with many configuration options, more guidance is needed to help an agent use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is high at 91%, so most parameters are documented in the schema. The description adds no additional parameter semantics beyond what's in the schema, such as explaining interactions between parameters or typical usage patterns. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('trigger the generation') and the resource ('a 360 skybox seamless image'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from its sibling 'post-skybox-upscale360-inferences', which might handle upscaling rather than base generation, leaving some ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, such as other skybox or image generation tools in the sibling list. It lacks context about prerequisites, typical use cases, or comparisons to similar tools like 'post-skybox-upscale360-inferences'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'trigger the upscaling,' implying a processing or mutation operation, but fails to detail critical aspects like permissions required, rate limits, side effects, or what the output entails (e.g., a job ID, asset, or error handling). This leaves significant gaps for safe and effective use.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and wastes no space, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex tool with 15 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain the behavioral context, output format, or usage scenarios, leaving the agent with insufficient information to operate the tool effectively despite the detailed input schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is high at 87%, meaning most parameters are documented in the schema itself. The description adds no additional parameter semantics beyond what the schema provides, such as explaining interactions between parameters like 'styleImages' and 'styleFidelity.' Given the high coverage, a baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Trigger the upscaling') and resource ('an image matching the 360 skyboxes specific geometry'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'post-upscale-inferences' or 'post-skybox-base360-inferences', which might handle similar operations, so it falls short of a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, such as other upscaling or skybox-related tools in the sibling list. It lacks context about prerequisites, typical use cases, or exclusions, leaving the agent with minimal direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'trigger a new image generation' which implies a write operation that consumes resources, but doesn't disclose critical behavioral traits like whether this is an asynchronous job, rate limits, authentication requirements, cost implications, or what happens on failure. For a complex generative AI tool with 19 parameters, this minimal description is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single sentence that gets straight to the point: 'Trigger a new image generation in Txt2Img mode.' There's no wasted words or unnecessary elaboration, making it easy to parse quickly. Every word earns its place by specifying both the action and the context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (19 parameters, no annotations, no output schema), the description is inadequate. It doesn't explain what the tool returns (images, job IDs, error formats), doesn't mention resource consumption or performance characteristics, and provides no guidance on parameter interactions. For a generative AI inference tool with many configuration options, this leaves too many gaps for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is high at 89%, so most parameters are well-documented in the schema itself. The description adds no parameter-specific information beyond what's in the schema, not even mentioning key required parameters like 'prompt' or 'modelId'. However, the high schema coverage means the baseline is 3, as the schema does the heavy lifting for parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Trigger a new image generation') and specifies the mode ('Txt2Img'), which distinguishes it from other image generation tools like 'post-img2img-inferences' or 'post-controlnet-inferences'. However, it doesn't explicitly differentiate from other txt2img variants like 'post-txt2img-ip-adapter-inferences' or 'post-txt2img-texture-inferences', which would require more specific context about what makes this base version unique.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling tools for different inference types (e.g., img2img, controlnet, ip-adapter variants), there's no indication of prerequisites, typical use cases, or comparisons to help an agent choose appropriately. The lack of context leaves the agent to infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool 'triggers a new image generation,' implying a write operation that creates assets, but doesn't disclose critical behaviors like whether this is an asynchronous job, what permissions are required, rate limits, or how results are returned (e.g., as asset IDs or direct images). The description lacks details on error handling, cost implications, or any side effects, which are essential for a generative tool with many parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without any wasted words. It directly states the action and key feature (IpAdapter reference image), making it easy to parse quickly. Every part of the sentence earns its place by conveying essential information in a compact form.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of the tool (26 parameters, no annotations, no output schema), the description is insufficiently complete. It doesn't address the tool's behavior, output format, error conditions, or integration with other tools. For a generative AI tool with many configuration options and no structured output documentation, the description should provide more context about what happens after triggering (e.g., returns a job ID or assets) and how to handle the results, which is currently missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is high at 81%, meaning most parameters are well-documented in the schema itself. The description adds minimal value beyond the schema by mentioning 'one IpAdapter reference image,' which hints at the 'ipAdapterImageIds' or 'ipAdapterImages' parameters but doesn't explain their semantics further. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't significantly enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Trigger a new image generation') and the mode ('Txt2Img mode with one IpAdapter reference image'), which specifies the verb and resource. It distinguishes itself from generic txt2img tools by mentioning the IpAdapter reference image, but it doesn't explicitly differentiate from sibling tools like 'post-img2img-ip-adapter-inferences' or 'post-controlnet-ip-adapter-inferences', which have similar IpAdapter functionality but different modes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose txt2img-ip-adapter over img2img-ip-adapter, controlnet-ip-adapter, or other image generation tools in the sibling list. There's no context about prerequisites, such as needing an existing IpAdapter image ID or understanding of the IpAdapter mechanism, leaving the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states 'Trigger a new... generation,' implying a write operation that creates assets, but fails to mention critical aspects like whether this is an asynchronous job, potential costs, rate limits, authentication requirements, or what happens on failure. For a complex generative tool with 18 parameters, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly. Every word earns its place by specifying the action and context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (18 parameters, no annotations, no output schema), the description is inadequate. It doesn't explain the return values, error handling, or behavioral traits like job queuing or asset management. For a generative AI tool with many tunable parameters, more context is needed to guide effective usage beyond the basic purpose.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is high (89%), so the schema already documents most parameters well. The description adds no additional parameter semantics beyond implying 'seamless texture' generation, which might hint at use cases but doesn't clarify specific parameters like 'originalAssets' or 'concepts.' With high schema coverage, the baseline is 3, and the description doesn't compensate with extra insights.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Trigger a new seamless texture image generation') and the mode ('in Txt2Img mode'), which specifies the verb and resource. It distinguishes from general 'post-txt2img-inferences' by focusing on 'seamless texture,' but doesn't explicitly differentiate from other texture-related siblings like 'post-texture-inferences' or 'post-img2img-texture-inferences' beyond the mode.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, such as other texture generation tools (e.g., 'post-texture-inferences' or 'post-controlnet-texture-inferences') or general Txt2Img tools. It lacks context about prerequisites, ideal scenarios, or exclusions, leaving the agent to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions creating 'temporary' URLs and supporting 'multipart uploads,' which hints at behavior, but lacks details on permissions, rate limits, expiration times, error handling, or whether this initiates a write operation. For a tool with 9 parameters and no annotations, this is insufficient behavioral disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded, consisting of just two sentences that directly state the tool's core functionality. Every word earns its place, with no redundancy or unnecessary elaboration, making it efficient for an agent to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (9 parameters, no annotations, no output schema), the description is incomplete. It doesn't cover return values, error conditions, or detailed behavioral traits. While the schema handles parameter documentation, the description fails to provide sufficient context for safe and effective use, especially for a tool that likely involves file uploads and mutations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is high (89%), so the schema already documents most parameters well. The description adds minimal value beyond the schema, as it doesn't explain parameter interactions or provide additional context. For example, it doesn't clarify how 'fileName,' 'fileSize,' 'parts,' and 'contentType' relate to multipart uploads versus other parameters like 'url.' Baseline 3 is appropriate given the schema's thoroughness.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Create a temporary upload URL for a file. Support multipart uploads. Return a list of URLs for each part of the file.' It specifies the verb ('create'), resource ('temporary upload URL'), and key capabilities (multipart support, returns URLs). However, it doesn't explicitly differentiate from sibling tools like 'post-asset' or 'post-uploads-action', which might handle similar upload-related functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling tools like 'post-asset' and 'post-uploads-action', there's no indication of scenarios, prerequisites, or exclusions. The agent must infer usage from the description alone, which lacks contextual direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. 'Add assets' implies a mutation operation, but it doesn't specify whether this requires authentication, what happens on duplicate asset IDs (e.g., ignored or error), if there are rate limits, or the response format. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste—'Add assets to a specific collection' directly conveys the core action without unnecessary words. It's appropriately sized for a simple tool and front-loaded with the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's mutation nature, lack of annotations, no output schema, and incomplete parameter documentation (50% schema coverage), the description is insufficient. It doesn't address key aspects like error handling, response format, or behavioral nuances (e.g., idempotency), leaving the AI agent with significant gaps in understanding how to use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50% (only 'collectionId' has a description), and the description adds no additional parameter information beyond what's implied by the tool name. It doesn't explain the format of 'assetIds' (e.g., UUIDs, maximum array size) or provide examples. With low schema coverage, the description fails to compensate, resulting in minimal added value over the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Add assets to a specific collection' clearly states the verb ('Add') and resource ('assets to a specific collection'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'put-asset-by-asset-id' (which modifies individual assets) or 'post-collection' (which creates collections), leaving room for confusion about when to choose this specific tool over similar ones.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., existing collection ID), exclusions (e.g., cannot create collections), or compare to siblings like 'put-assets-tags-by-asset-id' for tagging assets. This lack of context makes it harder for an AI agent to select this tool appropriately in complex scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states 'Add/delete tags' implying mutation, but fails to disclose critical behavioral traits such as required permissions, whether changes are reversible, error handling (beyond the schema's 'strict' parameter), or rate limits. This is inadequate for a mutation tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words, making it easy to parse. It is appropriately sized and front-loaded, directly stating the core functionality without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (mutation with 4 parameters), lack of annotations, no output schema, and low schema description coverage, the description is insufficient. It does not cover behavioral aspects, parameter details beyond basics, or expected outcomes, making it incomplete for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50% (only 'assetId' and 'strict' have descriptions). The description adds minimal value by implying 'add' and 'delete' parameters exist, but does not explain their semantics (e.g., tag format, idempotency, interactions). With low schema coverage, it partially compensates but leaves key parameters undocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Add/delete tags on a specific asset' clearly states the action (add/delete) and resource (tags on a specific asset), making the purpose immediately understandable. However, it does not explicitly differentiate from sibling tools like 'put-models-tags-by-model-id' or 'put-asset-by-asset-id', which handle tags on models or general asset updates respectively, so it misses full sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites (e.g., asset existence), exclusions, or compare to related tools like 'get-tags' for reading tags or 'put-asset-by-asset-id' for broader asset updates, leaving usage context unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It states 'Update' which implies a mutation, but lacks details on permissions, side effects, error handling, or response format. This is insufficient for a mutation tool without annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence with no wasted words, making it highly concise and front-loaded. It efficiently communicates the core action without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations and no output schema, the description is incomplete. It does not cover behavioral aspects like success/error responses, idempotency, or how it differs from similar update tools in the sibling list, leaving gaps for agent usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already documents both parameters ('collectionId' and 'name'). The description adds no additional meaning beyond what the schema provides, such as format constraints or examples, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Update') and the resource ('name of a Collection'), making the purpose evident. However, it does not differentiate this tool from other 'put-' or update-related sibling tools like 'put-asset-by-asset-id' or 'put-models-by-model-id', which limits its specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. For instance, it does not mention prerequisites like needing an existing collection ID or compare it to sibling tools like 'post-collection' for creation or 'delete-collections-by-collection-id' for removal.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. 'Add models' implies a write/mutation operation, but it doesn't disclose behavioral traits like whether this requires authentication, what happens on duplicate model IDs, if it's idempotent, rate limits, or error conditions. The description is minimal and lacks necessary context for safe invocation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded and directly states the tool's purpose without unnecessary elaboration, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations, no output schema, and incomplete parameter documentation, the description is insufficient. It lacks critical information about behavior, error handling, and output format, leaving significant gaps for an AI agent to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50% (only 'collectionId' has a description). The description adds no parameter semantics beyond what's implied by the tool name. It doesn't explain the format of 'collectionId' or 'modelIds', constraints, or examples. With low schema coverage, the description fails to compensate adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Add models') and target resource ('to a specific collection'), making the purpose understandable. It distinguishes from deletion tools but doesn't explicitly differentiate from similar 'put' operations like 'put-models-by-model-id' or 'put-assets-by-collection-id'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. While the name implies it's for adding models to collections, there's no mention of prerequisites, constraints, or comparison to sibling tools like 'post-models' or 'put-collections-by-collection-id'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It mentions updating and deleting descriptions, implying mutation, but fails to detail permissions, side effects, error handling, or response format. This leaves significant gaps in understanding the tool's behavior and risks.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and wastes no space, making it highly concise and well-structured for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the mutation nature of the tool, lack of annotations, and no output schema, the description is insufficient. It does not cover behavioral aspects like permissions, side effects, or what happens on success/failure, leaving the agent with incomplete context for safe and effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters well. The description adds minimal value by implying the 'description' parameter can be set to null for deletion, but this is also covered in the schema. Baseline 3 is appropriate as the schema handles most semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Update') and resource ('markdown description of the given modelId'), making the purpose specific and understandable. However, it does not explicitly differentiate from sibling tools like 'put-models-by-model-id' or 'put-models-examples-by-model-id', which might also update model-related data, so it falls short of a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, such as other 'put-models-*' tools for updating different aspects of a model. It lacks context on prerequisites, exclusions, or specific scenarios, leaving usage unclear beyond the basic action.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While 'Add/delete tags' implies a mutation operation, it doesn't disclose important behavioral traits like permission requirements, whether changes are reversible, error conditions beyond what's in the schema, or what happens when both 'add' and 'delete' arrays are provided. The description adds minimal value beyond the basic action statement.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just 7 words, front-loading the core action and target. Every word earns its place with zero waste or redundancy, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with 4 parameters, 50% schema coverage, no annotations, and no output schema, the description is inadequate. It doesn't explain the tool's behavior with partial parameters, error conditions, return values, or how it fits within the broader tag management system. The agent would struggle to use this tool correctly without additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50% (only 'modelId' and 'strict' have descriptions). The description mentions 'tags' which relates to the 'add' and 'delete' parameters, providing some semantic context beyond the schema. However, it doesn't explain the relationship between 'add' and 'delete' arrays, tag format requirements, or constraints on tag values, leaving significant gaps in parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Add/delete tags') and target resource ('for the given modelId'), providing a specific verb+resource combination. However, it doesn't distinguish this tool from sibling tools like 'put-assets-tags-by-asset-id' or explain how it differs from other tag-related operations in the system.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. There are multiple sibling tools that manipulate tags (like 'put-assets-tags-by-asset-id'), but the description doesn't mention any context, prerequisites, or comparison to help an agent choose appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Replace' implies a mutation, the description doesn't specify whether this operation overwrites existing data, requires specific permissions, has side effects, or returns any output. For a mutation tool with zero annotation coverage, this lack of behavioral context is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence with no wasted words. It front-loads the core action and resources, making it efficient and easy to parse. Every part of the sentence contributes to understanding the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (8 parameters, mutation operation) and lack of annotations or output schema, the description is insufficient. It doesn't explain what 'Replace' entails, how parameters like 'data', 'assetId', or 'preset' affect the operation, or what happens upon success or failure. For a tool with this level of complexity, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions 'trainingImageId' and 'modelId' but doesn't explain their semantics beyond what's in the schema. With high schema description coverage (88%), the schema already documents most parameters well. The description adds no additional meaning about parameter usage, interactions, or constraints, so it meets the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Replace') and the target resource ('trainingImageId for the given modelId'), making the purpose understandable. However, it doesn't explicitly differentiate this tool from sibling tools like 'put-models-training-images-pairs-by-model-id' or 'delete-models-training-images-by-model-id-and-training-image-id', which handle similar resources but different operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, such as whether the model or training image must exist, or compare it to related tools like 'post-models-training-images-by-model-id' (which likely creates training images) or 'delete-models-training-images-by-model-id-and-training-image-id' (which deletes them).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It indicates a 'Replace all' operation, which implies a destructive mutation (overwriting existing pairs), but doesn't specify critical details like whether this requires special permissions, if the operation is idempotent, what happens on failure, or if there are rate limits. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence with zero wasted words. It front-loads the key action ('Replace all training image pairs') and specifies the target ('for the given modelId'), making it highly efficient and easy to parse. Every word earns its place by conveying essential information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a mutation operation with 2 parameters, 50% schema coverage, no annotations, and no output schema), the description is incomplete. It lacks details on behavioral aspects (e.g., permissions, idempotency), parameter usage beyond basics, and expected outcomes. For a tool that replaces all training image pairs—a potentially significant change—more context is needed to ensure safe and correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50% (only 'modelId' has a description in the schema, while 'body' lacks one). The description mentions 'training image pairs' and implies they are provided via 'body', adding some context about what 'body' contains. However, it doesn't detail the structure or constraints of the pairs (e.g., that 'sourceId' and 'targetId' must be training assets, as noted in the schema), so it partially compensates but doesn't fully bridge the coverage gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Replace all training image pairs') and the target resource ('for the given modelId'), making the purpose specific and understandable. However, it doesn't explicitly distinguish this tool from sibling tools like 'put-models-training-images-by-model-id-and-training-image-id' (which appears to update individual pairs) or 'post-models-training-images-by-model-id' (which likely adds pairs), leaving some ambiguity about when to choose this exact tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., whether the model must exist or be in a specific state), exclusions, or comparisons to sibling tools like 'post-models-training-images-by-model-id' (add) or 'delete-models-training-images-by-model-id-and-training-image-id' (remove). This lack of context makes it difficult for an agent to decide when this tool is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states this is a read operation ('Get'), which is clear, but it doesn't disclose behavioral traits like authentication requirements, rate limits, error handling, or what happens if the collection ID is invalid. For a tool with no annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It is front-loaded and directly states the tool's purpose, making it easy to parse quickly. Every word earns its place by conveying essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (single parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on usage context, behavioral traits, and output format, which are important for a tool with no structured metadata. This leaves gaps in completeness for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'collectionId' documented as 'The collection ID to retrieve'. The description adds no additional meaning beyond this, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'details of a collection', making the purpose understandable. However, it doesn't differentiate from sibling tools like 'get-collections' (which likely lists multiple collections) or specify what details are included, leaving some ambiguity compared to alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as 'get-collections' (for listing collections) or other get-* tools. The description implies usage when you have a specific collection ID, but it doesn't explicitly state this or mention prerequisites, leaving the agent to infer context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states this is a list operation, implying it is likely read-only and non-destructive, but does not confirm this or describe other traits like pagination, rate limits, authentication needs, error conditions, or what 'examples' entail (e.g., metadata, images). The description adds minimal context beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is appropriately sized for a simple listing tool and front-loaded with the core action, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (a read operation with 2 parameters), 100% schema coverage, and no output schema, the description is minimally adequate. It states what the tool does but lacks details on behavior, output format, or error handling. Without annotations, it should provide more context (e.g., safety, response structure) to be fully helpful, but it meets the bare minimum for a simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions the 'modelId' parameter, aligning with the input schema which has 100% coverage and describes both parameters ('modelId' and 'originalAssets'). However, it does not add meaning beyond the schema—it does not explain what 'examples' are in relation to 'originalAssets' or provide usage examples. With high schema coverage, the baseline is 3, as the description does not compensate with extra insights.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'List all examples of the given `modelId`' clearly states the verb ('List') and resource ('examples'), specifying the scope as 'all' for a given modelId. It distinguishes from general listing tools like 'get-models' by focusing on examples for a specific model, but does not explicitly differentiate from potential sibling tools that might also retrieve examples in different ways (e.g., filtered examples).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites (e.g., needing a valid modelId), exclusions (e.g., not for filtered examples), or compare to siblings like 'get-models' (general models) or 'get-models-by-model-id' (specific model details). Usage is implied by the parameter name but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a read operation ('Get'), implying it's non-destructive, but doesn't cover critical aspects like authentication needs, rate limits, error handling, or response format. For a tool with zero annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (single parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavioral traits and usage context, making it incomplete for optimal agent guidance despite the simple structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the single parameter 'uploadId' fully documented in the schema. The description adds no additional meaning beyond what the schema provides (e.g., format examples, sourcing hints), so it meets the baseline of 3 where the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('details of an existing upload'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get-assets-by-asset-id' or 'get-workflows-by-workflow-id' that follow similar patterns for different resources, missing full sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an uploadId from a previous operation), exclusions, or comparisons to other 'get-' tools for different resources, leaving usage context implied at best.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the tool as triggering image generation, implying a write operation, but does not address critical behavioral aspects such as permissions required, rate limits, whether the operation is asynchronous or returns immediate results, or potential side effects like resource consumption. The description adds minimal context beyond the basic action, leaving significant gaps for an agent to understand how to invoke it safely and effectively.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, consisting of two sentences that directly state the tool's purpose and key parameter roles. There is no wasted text or redundancy. However, it could be slightly more structured by explicitly naming critical parameters or linking to sibling tools, but it efficiently conveys essential information without verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (33 parameters, no annotations, no output schema), the description is inadequate. It lacks information on output format, error handling, dependencies, or how results are returned. While the schema covers many parameters, the description does not address the tool's broader context, such as integration with other tools or typical use cases. For a generative AI tool with many parameters and no structured behavioral hints, the description should provide more guidance to ensure correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is high at 85%, providing detailed parameter documentation. The description adds some semantic context by explaining the roles of 'mask' and 'second reference image' (implied as IPAdapter image), which helps clarify the core parameters. However, it does not elaborate on other key parameters like 'prompt' or 'modelId', nor does it compensate for the 15% coverage gap (e.g., parameters like 'dryRun' or 'originalAssets' lack schema descriptions). The baseline is 3 due to high schema coverage, with the description adding marginal value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Trigger a new image generation in Inpaint + IpAdapter mode.' It specifies the verb ('trigger'), resource ('image generation'), and mode ('Inpaint + IpAdapter'), distinguishing it from generic image generation tools. However, it does not explicitly differentiate from sibling tools like 'post-inpaint-inferences' or 'post-img2img-ip-adapter-inferences', which limits the score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides implied usage by explaining the roles of the mask and reference image: 'The mask indicates the area to inpaint in the reference image, and the second reference image is used as an IPAdapter to guide the inpainting.' This gives context on when to use it for inpainting with IPAdapter guidance. However, it lacks explicit guidance on when to choose this tool over alternatives like 'post-controlnet-inpaint-ip-adapter-inferences' or 'post-img2img-ip-adapter-inferences', and does not mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states it's a 'Get' operation, implying read-only behavior, but doesn't disclose any behavioral traits like rate limits, authentication needs, or response format. This leaves significant gaps for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded and wastes no space, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool with no output schema, the description is minimally adequate but lacks depth. It doesn't explain what 'price details' include (e.g., rates, tiers, currency) or behavioral aspects, leaving the agent with incomplete context despite the simple input schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0 parameters and 100% schema description coverage, the description doesn't need to add parameter details. It appropriately focuses on the tool's purpose without redundant information, aligning with the baseline expectation for zero-parameter tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and resource ('public Prepaid Compute Units (or OSCU) price details'), making the purpose specific and understandable. It doesn't explicitly differentiate from siblings, but given the unique resource focus, it's sufficiently clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives, such as other 'get-' tools for pricing or usage data. The description implies it's for public price details but doesn't specify contexts like budgeting or comparison.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool triggers image generation but lacks details on permissions, rate limits, costs, or response format. The description is minimal and does not compensate for the absence of annotations, leaving significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, consisting of two clear sentences that directly state the tool's purpose and the roles of key inputs. There is no wasted verbiage, making it efficient and easy to understand.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (29 parameters, no annotations, no output schema), the description is insufficient. It lacks information on behavioral traits, usage context, and output expectations, failing to provide a complete understanding for an AI agent to invoke the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is high at 93%, so the baseline is 3. The description adds minimal value by mentioning control and reference images, which loosely map to 'controlImage' and 'image' parameters, but it does not provide additional semantic context beyond what the schema already documents in detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Trigger a new image generation in ControlNet + Img2Img mode.' It specifies the verb ('trigger'), resource ('image generation'), and mode ('ControlNet + Img2Img'), distinguishing it from siblings like 'post-controlnet-inferences' or 'post-img2img-inferences' by combining both approaches.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions the roles of control and reference images but does not specify scenarios, prerequisites, or exclusions compared to other image generation tools in the sibling list, such as 'post-controlnet-inferences' or 'post-img2img-inferences'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the tool triggers image generation and describes the mask's function, but fails to cover critical aspects like authentication requirements, rate limits, cost implications, error handling, or the nature of the output (e.g., whether it returns generated images or a job ID). This is inadequate for a complex mutation tool with 26 parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with only two sentences, front-loading the core purpose without any redundant information. Every sentence earns its place by clearly stating the action and key parameter role, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (26 parameters, no annotations, no output schema), the description is insufficient. It lacks details on behavioral traits (e.g., side effects, response format), usage context, and does not explain return values, leaving significant gaps for an AI agent to understand how to invoke and interpret results effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds minimal parameter semantics by explaining the mask's purpose ('indicates the area to inpaint'), but with 92% schema description coverage, most parameters are already well-documented in the input schema. The description does not compensate for the few gaps (e.g., 'dryRun' lacks a schema description) or provide high-level context beyond what the schema offers, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Trigger') and resource ('image generation in Inpaint mode'), and distinguishes it from siblings by specifying the mode (Inpaint) and the role of the mask, which is unique among sibling tools like post-img2img-inferences or post-txt2img-inferences.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, such as other inference tools like post-img2img-inferences or post-controlnet-inpaint-inferences. It mentions the mask's role but does not specify scenarios, prerequisites, or exclusions for usage, leaving the agent without contextual direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context about time range limits and granularity calculation, which aren't in the schema. However, it doesn't cover other important aspects like response format, pagination, error handling, or rate limits, which are critical for a tool with 8 parameters and no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, with the first sentence stating the core purpose and examples. The second sentence adds necessary constraints without redundancy. Both sentences earn their place by providing distinct information, though it could be slightly more structured for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (8 parameters, no annotations, no output schema), the description is moderately complete. It covers the purpose and some behavioral constraints but lacks details on output format, error cases, and full usage scenarios. This leaves gaps that could hinder an agent's ability to use the tool effectively without trial and error.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so all parameters are documented in the schema itself. The description adds minimal value beyond the schema by implying filtering capabilities and time range constraints, but it doesn't provide additional syntax, format details, or examples. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Provide usage data for the given filters. Such as consumed compute units, number of assets generated, etc.' It specifies the verb ('provide') and resource ('usage data') with concrete examples, making it easy to understand. However, it doesn't explicitly differentiate from sibling tools like other 'get-' tools (e.g., get-assets, get-models), which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides some implied usage context by mentioning time range constraints ('Maximum time range... is 120 days') and granularity calculation, which helps set expectations. However, it lacks explicit guidance on when to use this tool versus alternatives (e.g., other data retrieval tools) or any prerequisites, leaving gaps in optimal usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the creation of a new Asset with children, which is useful behavioral context. However, it lacks details on permissions, rate limits, error handling, or the processing time. The description doesn't contradict annotations, but it's incomplete for a mutation tool with no structured safety hints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by a bulleted list of outputs and a concise explanation of the result. Every sentence earns its place with no redundant information, making it efficient and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (10 parameters, mutation operation) and lack of annotations or output schema, the description is moderately complete. It covers the purpose and output structure but misses critical details like error conditions, performance expectations, or deeper parameter interactions. It's adequate as a minimum viable description but has clear gaps for a tool with many parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is high (90%), so the baseline is 3. The description adds value by clarifying the output structure (new Asset with texture maps as children + original as Albedo map), which isn't covered in the input schema. It doesn't detail individual parameters, but the high schema coverage and added output context justify a score above baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Trigger the conversion') and resource ('an image texture'), listing the exact texture maps produced (Height, Normal, Smoothness, Metallic, Edge, Ambient Occlusion) and the outcome (new Asset with children). It distinguishes from siblings like 'post-caption-inferences' or 'post-remove-background-inferences' by focusing on texture map generation from an image.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives is provided. While the description implies it's for converting image textures to maps, it doesn't mention prerequisites (e.g., input texture requirements) or compare to similar tools like 'post-controlnet-texture-inferences' or 'post-img2img-texture-inferences' from the sibling list, leaving usage context unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions the tool is deprecated (a critical behavioral trait), it lacks essential operational details such as required permissions, rate limits, cost implications, or what the output looks like (e.g., returns an asset ID, URL, or processed image). For a complex mutation tool with 22 parameters, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with two sentences: one stating the purpose and capabilities, and another providing critical deprecation and migration guidance. The note is front-loaded with important information, though the second sentence could be slightly more concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (22 parameters, no output schema, no annotations), the description is incomplete. It lacks details on output format, error handling, authentication needs, and operational constraints. While the deprecation note is crucial, it doesn't compensate for the missing behavioral and contextual information required for effective tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is high at 91%, so the schema already documents most parameters thoroughly. The description adds minimal value beyond the schema by vaguely mentioning 'styles and presets' and 'craft your very own settings,' but doesn't provide additional syntax, format, or interaction details for the parameters. The baseline of 3 is appropriate given the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Trigger the upscaling of an image') and resource ('image'), distinguishing it from sibling tools like 'post-img2img-inferences' or 'post-restyle-inferences' that perform different image transformations. The mention of 'styles and presets' further clarifies the tool's functionality beyond basic upscaling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly provides when-not-to-use guidance by stating the tool is deprecated and recommending an alternative ('POST /generate/custom/{modelId}' with specific parameters). It includes a link to documentation for further details, offering clear direction for migration.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool is for generation (implying a write operation) and notes availability constraints for certain models, which is useful behavioral context. However, it lacks details on permissions, rate limits, or what the generation entails (e.g., whether it's async, returns a job ID). The description doesn't contradict annotations, as there are none.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by essential notes and alternatives. It avoids unnecessary fluff, though the note about model availability could be more concise. Every sentence adds value, making it efficient overall.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (generation with custom models), lack of annotations, no output schema, and incomplete parameter documentation, the description is moderately complete. It covers key usage scenarios and constraints but misses details on behavioral aspects like error handling or response format. It's adequate but has clear gaps for a tool with 3 parameters and significant functionality.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 33% (only the 'body' parameter has a description). The description adds meaning by explaining that the 'body' must be retrieved from 'GET /models/{modelId}' inputs fields, which clarifies its purpose beyond the schema. However, it doesn't address 'dryRun' or 'modelId' parameters, leaving gaps. With low schema coverage, the description partially compensates but not fully.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate with any model (Image, Video, Audio, 3d).' It specifies the action (generate) and resource types (models for various media). However, it doesn't explicitly differentiate from sibling tools like 'post-txt2img-inferences' or 'post-img2img-inferences' that handle specific model types, though it implies a broader scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidelines: it tells when to use this tool (for any model generation) and when not to use it (for SD1.5, SDXL, Flux.1 and Flux.1-Kontext based models, where alternative endpoints like 'POST /generate/txt2img' should be used). It also mentions retrieving model inputs from another endpoint, adding context for prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that the tool checks configuration status without exposing values, which is useful behavioral context. However, it doesn't mention potential outcomes (e.g., returns boolean, error states), performance characteristics, or authentication requirements, leaving gaps for a tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose and includes a clarifying parenthetical. Every word serves a purpose with zero waste, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool with no annotations and no output schema, the description provides adequate basic context about what the tool does. However, it lacks details on return format (e.g., what 'check' returns), error handling, or dependencies, which would be helpful given the absence of structured output information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so no parameter documentation is needed. The description appropriately focuses on the tool's purpose rather than parameters, earning a baseline score of 4 for not adding unnecessary param info.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Check if credentials are configured') and resource ('for this session'), with explicit clarification about what it does not do ('does not expose actual values'). This distinguishes it from sibling tools like 'set-credentials' which configures credentials rather than checking their status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by mentioning 'this session' and the non-exposure of values, suggesting it's for verification rather than retrieval. However, it doesn't explicitly state when to use it versus alternatives like checking credential validity through other means or what happens if credentials are unconfigured.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It clearly indicates this is a session configuration tool (not a data operation), specifies it must be called first (prerequisite behavior), and provides the credential source. However, it doesn't mention whether credentials persist across sessions, if there are rate limits for configuration, or what happens on failure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences with zero waste: first states the purpose, second provides critical usage requirement, third gives credential source. Each sentence earns its place by providing essential information for proper tool invocation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 2-parameter configuration tool with no annotations and no output schema, the description is mostly complete: it explains purpose, mandatory usage order, and credential source. However, it doesn't describe what happens after configuration (e.g., confirmation message, error handling) or whether there are validation requirements beyond the schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents both parameters (api_key and api_secret). The description adds marginal value by mentioning where to obtain credentials ('Get your credentials from https://app.scenario.com/') but doesn't provide additional semantic context about parameter usage beyond what's in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Configure your Scenario.com API credentials') and resource ('for this session'), distinguishing it from all sibling tools which perform operations on assets, models, collections, etc. It explicitly identifies this as a setup/configuration tool rather than a data operation tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'You MUST call this tool first before using any other Scenario tools.' It also specifies when to use it (for credential setup) and includes a clear alternative action ('Get your credentials from https://app.scenario.com/') for obtaining the required parameters.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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