TokenLab MCP Server

Server Quality Checklist

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Latest release: v0.6.4
Disambiguation2/5
Significant overlap exists: two tools for creating images (create_image, create_image_file) and two for editing images (edit_image, edit_image_file), plus multiple chat completion tools (create_chat_completion, create_anthropic_message, create_gemini_content, create_response) that serve similar purposes but differ in API shape. Agents may struggle to choose the correct tool without reading detailed descriptions.
Naming Consistency4/5
All tools use snake_case with a verb_noun pattern (e.g., create_video, list_models). The naming is mostly consistent, but minor issues arise with duplicate verbs like 'create' for similar tasks (create_image vs create_image_file) and mixed verb tenses (get vs retrieve). Overall, the pattern is predictable.
Tool Count3/5
With 31 tools, the count is somewhat high for the server's scope (a unified AI API gateway). While many operations are covered, there is redundancy (e.g., four completion tools, two image creation tools) that could be consolidated. A more streamlined set of 20-25 tools would be more appropriate.
Completeness4/5
The tool set covers a broad range of AI operations: text completion, image generation/editing, audio, video, 3D models, embeddings, file management, and model info. Minor gaps exist (e.g., no tool to list async tasks, no update for files), but overall, the surface is comprehensive for the domain.
Average 3.2/5 across 31 of 31 tools scored. Lowest: 1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 50 commits in the last 12 weeks
- Last stable release on July 13, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?
The description adds no behavioral details beyond what annotations already indicate; no mention of file size limits, content type, or other operational traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness1/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extreme under-specification; three words do not constitute a useful 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 no output schema and no parameter details, the description is completely inadequate for an agent to correctly invoke the tool.
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?
With 0% schema description coverage, the description fails to explain the required 'file_id' parameter, its format, or how to obtain it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose1/5
Does the description clearly state what the tool does and how it differs from similar tools?
Tautological: description restates name/title.
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; there is no mention of context, 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?
Annotations already indicate read-only, idempotent, and open-world hints. The description adds no further behavioral context such as authentication needs, return format, or what 'retrieve' entails.
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?
Overly brief to the point of being uninformative. It is not concise in a helpful way—it fails to convey any useful information beyond 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?
Without an output schema, the description should explain what is returned. It does not. With two parameters and no behavioral details, the description is grossly incomplete for an AI agent to understand its usage.
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 coverage is only 50% (file_id lacks a description). The tool description does not clarify the meaning of file_id or the purpose of the 'anthropic-beta' parameter beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose1/5
Does the description clearly state what the tool does and how it differs from similar tools?
Tautological: description restates name/title.
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. For example, it does not clarify how it differs from 'retrieve_file_content' or 'list_files'.
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?
The description adds no behavioral context beyond the annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint). Annotations already indicate safety, but the description fails to mention pagination, cursor usage, or beta header requirements.
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?
Extremely short but not informative. It is under-specified, wasting the opportunity to provide essential context in a concise manner.
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?
With 7 parameters, no output schema, and complex features (pagination cursors, purpose enum, beta header), the description is completely inadequate. An agent cannot determine behavior like filtering, pagination limits, or mode switching.
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 71% (below 80%), so the description should compensate but does not. It adds no meaning beyond what the schema already provides for parameters like 'purpose', 'after', 'before_id', etc.
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?
Tautological: description restates name/title.
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 (e.g., retrieve_file for a single file). No context on prerequisites or typical 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?
Annotations already indicate non-readonly, non-idempotent, non-destructive, open-world. The description adds no new behavioral context beyond 'creates an image', which is consistent but redundant. No contradiction found.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, but it wastes words by repeating the title. While short, it could be more informative without increasing length.
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 (24 parameters, no output schema, minimal annotations), the one-sentence description is grossly inadequate. It fails to address usage context, return values, or operational details.
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?
With 24 parameters and only 21% schema description coverage, the description provides no parameter details. It does not explain any parameter's purpose, thus failing to compensate for the schema's low coverage.
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 merely restates the tool's name ('Create image Creates an image') without specifying what distinguishes it from sibling tools like 'create_image' or 'edit_image'. It lacks a clear verb-resource pair that differentiates its 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?
No guidance is provided on when to use this tool versus alternatives (e.g., create_image, edit_image), nor any conditions for appropriate use. The agent receives no contextual decision support.
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?
Annotations indicate readOnlyHint=false (mutation) and openWorldHint=true (accepts extra parameters), but the description adds little beyond mentioning input methods. It does not disclose behavior like error handling, rate limits, or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short but contains redundancy (repeating 'Edit image' in the first sentence). It could be more concise but is not overly verbose.
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?
With 19 parameters, nested objects, no output schema, and low schema coverage, the description is woefully incomplete. It does not explain return values, required parameter combinations, or constraints.
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 only 32%, and the description fails to explain the many undocumented parameters. It only mentions the three input methods, leaving agents unclear on how to use size, quality, background, etc.
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 edits images and mentions three input methods (multipart, JSON URLs, images array) for supported GPT Image models. However, it does not explicitly distinguish this tool from sibling tools like create_image or edit_image_file.
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 vs other image-related tools. The description does not specify prerequisites, recommended scenarios, or when to choose alternative tools.
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?
Annotations already indicate non-read-only behavior. The description adds context about input methods (multipart, JSON URLs, etc.) but does not disclose side effects, permissions, or what happens to the original image.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single run-on sentence with redundancy ('Edit image Edits an image'). It could be more 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 18 parameters and no output schema, the description is too brief. It omits return values, step-by-step usage, and model-specific behaviors for non-GPT models.
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?
With only 22% schema description coverage, the description does not add meaning to individual parameters beyond the schema's few descriptions. It mentions input methods but does not help understand the many 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 clearly states the tool edits an image, but it is redundant ('Edit image Edits an image') and does not differentiate from the sibling 'edit_image' tool, causing confusion.
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 vs alternatives like 'edit_image' or 'create_image_file'. The description lacks any context about when to prefer this tool.
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?
Annotations are present (readOnlyHint=false, openWorldHint=true), but the description adds no additional behavioral context beyond 'translates text'. It does not disclose synchronous/asynchronous behavior, auth needs, rate limits, or any side effects hinted by openWorldHint=true.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, but the first sentence 'Translate text' is redundant with the title. Could be merged into one concise sentence. Overall efficient but not optimally 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?
With 6 parameters and no output schema, the description is insufficient. It does not explain return format, error handling, or any additional context like nesting or enum details (only mime_type has enum).
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. It mentions 'text' and 'target_language' implicitly, but does not explain 'model', 'user', 'mime_type', or 'source_language'. Only a fraction of the parameters are addressed.
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?
Description clearly states it translates text into a target language, which is a specific verb+resource. However, it does not differentiate from sibling tools like translate_audio beyond the resource type (text vs audio), and the phrase 'using the current translation request format' is vague.
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 vs alternatives like translate_audio or other translation tools. No when-not-to-use or prerequisite information provided.
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?
Annotations (non-readOnly, non-idempotent) are consistent with the description stating a creation action. The description adds that it uses Tripo3D and returns a task ID, but does not disclose side effects, authorization needs, or polling behavior details. With annotations present, the bar is lower, but the description adds minimal context beyond the annotations.
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 short but contains a redundant phrase ('Create 3D model generation task' repeated). It is not optimally structured and could be more concise without losing 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?
With 9 parameters (1 required) and no output schema, the description only mentions returning a task ID. It does not explain how to poll for results, the role of optional parameters, or how they affect the output. This is insufficient for a complex tool.
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% (5 of 9 params have descriptions). The description does not document any parameters or add meaning beyond what the schema already provides. It fails to compensate for undocumented parameters like model, format, quality, and image_url.
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 that the tool creates a 3D model generation task using Tripo3D and returns a task ID for polling. This distinguishes it from siblings like create_image or create_video. However, the first sentence is slightly redundant, repeating 'Create 3D model generation task' twice.
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 compared to siblings (e.g., create_image, create_video). There is no mention of prerequisites, when not to use it, or alternatives.
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?
Annotations are limited (readOnlyHint=false, no destructive hint). Description adds minimal behavioral info: only that it creates an image from a prompt. Does not disclose async capability, model-specific behaviors, or that it can return multiple images. For a mutation tool, more transparency expected.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence is concise but wastes words repeating the name. Could be more structured to front-load key info like 'Generates images from prompts with model-specific options.'
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?
Very incomplete for a 24-parameter tool. No mention of return format, async option, model recommendations, or that many parameters are model-specific. Only covers the bare minimum.
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 coverage is 100%; description adds no additional meaning beyond 'given a prompt'. Baseline 3 is appropriate as schema already documents 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?
States verb 'create' and resource 'image' clearly. However, it doesn't distinguish from sibling 'create_image_file' which likely creates a file object rather than returning image data. The description is adequate but not specific about 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?
No guidance on when to use this tool vs alternatives like edit_image or create_image_file. No context about required model selection or prerequisites.
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?
Annotations already indicate readOnlyHint=false and destructiveHint=false. The description adds no behavioral details beyond the basic function. No mention of side effects, required permissions, or response behavior.
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 very short but contains repetition ('Rerank documents Reranks documents'), which reduces clarity. It could be condensed into one sentence without loss of meaning.
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?
No output schema is provided, and the description does not explain what the tool returns or how to interpret the reranked documents. Given the complexity of reranking, more context (e.g., ordering, scoring) would be 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 80%, so baseline is 3. The description does not add any parameter-level details beyond the schema. It only restates the tool's purpose, not parameter guidance.
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 'rerank' and the resource 'documents', and explains it uses semantic similarity for relevance ranking. It distinguishes from sibling tools which are mostly for generation, embedding, or file 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, nor does it mention when not to use it. It simply states what it does without usage context.
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?
Annotations indicate readOnlyHint=false (write), destructiveHint=false, and openWorldHint=true. The description adds that it returns a task ID for polling, implying asynchronous behavior. No contradiction with annotations found, but it does not disclose potential side effects, rate limits, or failure modes.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two sentences but is redundant: the first sentence repeats the title. It could be streamlined to one sentence without losing meaning.
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 (12 parameters, no output schema), the description is too sparse. It only mentions returning a task ID but does not explain how to use it, error handling, or success criteria. Essential context is missing for effective use.
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?
With 67% schema coverage (8 of 12 parameters described), the description itself adds no parameter information. It does not explain any parameter meanings beyond what the schema already provides, failing to compensate for the missing 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 states the tool creates a music generation task using Suno and returns a task ID for polling. It clearly identifies the verb and resource, and the context of 'music' distinguishes it from other creation tools like create_image or create_video. However, it could be more specific about the nature of the task.
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 create_audio or other creation tools. There is no mention of prerequisites, ideal use cases, or situations where it should not be used.
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?
Annotations indicate readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false. The description adds no behavioral context beyond these annotations, such as side effects or resource creation details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short but awkwardly constructed with 'Create transcription Transcribes...' It could be a single clear sentence. Information density is low, but no filler words.
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?
With 7 parameters, 2 required, and no output schema, the description provides minimal context. It does not explain return values, file handling, or limitations. The annotations are sparse, leaving 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 coverage is 100%, so all parameters have descriptions. The tool description does not add additional meaning beyond the schema, but it does not detract. Baseline 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 tool transcribes audio to text using 'Speech-to-Text'. It identifies the verb and resource, differentiating from sibling 'translate_audio'. However, the phrase 'Create transcription' is slightly redundant.
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 'translate_audio' or 'create_chat_completion'. No exclusions or prerequisites 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?
Annotations already indicate readOnlyHint=false (mutation), destructiveHint=false, openWorldHint=true. The description only says 'transcribes and translates to English', adding no behavioral context beyond annotations (e.g., file constraints, language support, result handling).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (one fragment). While front-loaded and concise, it lacks structure and omits important details. Not overly verbose but under-specified.
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?
With 5 parameters, no output schema, and low schema coverage, the description does not explain key aspects like model options, prompt usage, or output format. Significant gaps for a complex translation task.
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 coverage is 20% (only file has a description). The description does not clarify parameters like model, prompt, temperature, or response_format, leaving 4 parameters undocumented. Description fails to compensate for low schema 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 transcribes audio and translates to English, distinguishing it from siblings like transcribe_audio (transcription only) and translate_text (text input).
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 for audio-to-English translation tasks but does not explicitly state when to use it versus alternatives like transcribe_audio or translate_text, nor does it provide prerequisites or exclusions.
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?
The description reveals that the task is asynchronous and instructs clients to poll the poll_url first or use the fixed status endpoint. This adds behavioral context beyond the annotations (which only indicate readOnlyHint=false and not destructive). No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short (two sentences) but contains a redundant phrase at the beginning ('Create video generation task Creates'). It is not perfectly concise and could be streamlined.
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 46 parameters, 0 required, and no output schema, the description is too minimal. It does not explain the complex parameter interactions, operation types, or provide high-level context for how to construct a valid request. The parameter descriptions in the schema are detailed, but the tool description itself lacks 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 100%, so the schema already documents all parameters thoroughly. The tool description adds no additional parameter-level details beyond what is in the schema, earning the baseline score of 3.
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 creates an asynchronous video generation task, uses the verb 'create', and specifies the resource (video generation task). However, it starts with a redundant phrase 'Create video generation task' that restates the title, and does not distinguish from sibling creation tools like create_image or create_3d_model.
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 when not to use it or what context is appropriate for video generation versus other creation tasks.
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?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds that it returns the 'public pricing surface' and mentions optional filters, which is useful but does not disclose response format, pagination, or rate limits. Adds moderate value beyond annotations.
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?
Description is very short, but the phrasing 'List model pricing Returns...' appears to be two fragments without punctuation, slightly reducing clarity. However, it is still mostly concise and front-loaded with the main action.
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 read-only list tool with no output schema, the description could mention the response format or return type. It is adequate but not thorough; the annotations cover safety aspects.
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 coverage is 100% with both parameters having clear descriptions ('Filter by model tag.' and 'Filter by provider ID.'). The description merely repeats 'optional provider and tag filters' without adding new meaning, so baseline 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?
Description clearly states the tool lists model pricing for active models with optional filters. The verb 'List' and resource 'model pricing' are specific. Distinguishes from sibling 'get_model_pricing' by implying a comprehensive listing vs. potentially a single model, but not explicitly stated.
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. Does not mention when not to use or provide context about prerequisites or exclusions.
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?
Annotations already indicate this is a non-read-only, non-destructive, non-idempotent operation. Description confirms it creates an embedding but adds no additional behavioral context like cost, storage, or side effects. Adequate but not enhanced.
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?
Description is one sentence but begins with a redundant repetition of the title ('Create embeddings Creates...'). This wastes words without adding value. More concise phrasing would improve.
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?
No output schema is provided, and the description does not explain what is returned (e.g., vector, dimensions). Given the tool has 5 parameters and no return value documentation, the description is incomplete for an agent to fully understand 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 covers 100% of parameters with descriptions. Tool description adds no extra meaning beyond what's in the schema (e.g., just mentions 'input text'). Baseline score of 3 is appropriate.
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?
Description explicitly states 'Creates an embedding vector representing the input text,' clearly identifying the verb (creates) and resource (embedding vector). It distinguishes from sibling tools like create_multimodal_embedding by specifying 'text' input.
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?
Description implies usage for text embedding but provides no explicit guidance on when to use this tool versus alternatives such as create_multimodal_embedding or rerank_documents. No 'when to use' or 'when not to use' notes.
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?
Annotations indicate this is a non-read-only, non-destructive operation. The description accurately describes generation, but does not add behavioral context beyond the annotations, such as cost, latency, or side effects like file creation.
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 concise sentence that immediately conveys the tool's primary function. While very brief, it is appropriately front-loaded and contains no superfluous 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 11 parameters and no output schema, the description lacks crucial context such as return type (e.g., audio file), format details, or expected behavior for different model families. More detail is needed for practical 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 fully documents all 11 parameters. The tool description adds no additional meaning to parameters, maintaining the baseline score 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 title 'Create speech' and description 'Generates audio from the input text (Text-to-Speech)' clearly state the verb-resource relationship and the tool's specific function, distinguishing it from sibling tools like transcribe_audio or create_music.
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 usage guidance is provided. The description does not explain when to use this tool vs. alternatives (e.g., transcribe_audio) or mention any prerequisites, such as required voice model availability.
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?
Annotations indicate readOnlyHint=false and openWorldHint=true, and the description confirms a write operation. It mentions returning metadata for Anthropic mode but does not elaborate on persistence or other side effects. It adds some context but falls short of full transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences but starts with a redundant 'Upload file' that repeats the title. The second sentence is dense but informative. Could be trimmed for conciseness without losing 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?
No output schema is provided, but the description only mentions the return value for Anthropic Files mode. For the primary TokenLab/OpenAI mode, it does not specify the return format (e.g., a file object with id). This is a significant gap in completeness.
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 coverage is 100%, and the description adds meaningful context: explaining the purpose parameter's three values, the model parameter's default and binding behavior, and the anthropic-beta header's effect. This adds value beyond 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 tool uploads TokenLab/OpenAI-compatible batch or image files, and optionally Anthropic Files resource via a header. It distinguishes between two modes, but the initial repetition of 'Upload file' and mixing of modes could be clearer. No explicit sibling differentiation, but the purpose is well-defined.
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 context on when to use each mode (TokenLab/OpenAI vs Anthropic Files) based on the anthropic-beta header. However, it does not guide the agent away from alternatives like create_image_file or edit_image_file, nor does it specify prerequisites or limitations.
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?
Annotations already mark destructiveHint=true and readOnlyHint=false. The description adds context about the types of files it can delete (batch, bound image/reference) and the optional beta mode, but does not elaborate on irreversible nature or side effects.
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?
Two sentences; the first is redundant ('Delete file' repeats the title). Could be tighter, but overall brief 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.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple delete tool, the description covers the action and the special beta mode. However, it omits return value expected (no output schema), error behavior, and whether deletion is asynchronous. Adequate but with noticeable 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 coverage is 100% with descriptions for both parameters. The main description adds no additional semantics beyond the schema; it mentions the beta header in passing but does not enhance parameter understanding.
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 it deletes files, specifying types (batch file or bound image/reference file). It distinguishes from siblings like retrieve_file or list_files by its destructive action.
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 like retrieve_file or edit_image. The only usage hint is about the anthropic-beta header for a specific API mode, but no context on when deletion is appropriate or 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?
Annotations already indicate the tool is read-only, idempotent, and non-destructive. The description adds no further behavioral context (e.g., output format, rate limits, or open-world implications). With high annotation coverage, the description's lack of extra detail is acceptable, earning a baseline 3.
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 directly conveys the tool's purpose without extraneous information.
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?
Given the tool's simplicity, two parameters, and comprehensive annotations, the description covers the essential functionality of comparing model details and pricing. It does not describe the return structure, but the absence of an output schema makes this less critical. Slightly incomplete due to the missing parameter explanation, but overall adequate.
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. It clarifies that 'models' is a list of model IDs and that the tool compares details and pricing. However, it fails to explain 'include_raw', leaving one of two parameters undocumented. This partial coverage justifies a score of 2.
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 uses a specific verb 'Compare' and identifies the resource 'public TokenLab model details and pricing' for 'several model IDs'. This clearly distinguishes the tool from siblings such as 'get_model' (single) and 'list_models' (all).
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 for comparing multiple models but does not explicitly state when to use it versus alternatives or provide when-not-to-use guidance. A minimal viable score is appropriate as the 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.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint, openWorldHint, idempotentHint true, and destructiveHint false, covering safety and idempotency. The description adds 'agent-readable API overview' but does not elaborate on behavior beyond that. It does not contradict 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 sentence with no extraneous words. It is front-loaded and efficient.
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 has no parameters and annotations cover safety, the description is minimal. However, it does not describe the return value or what the 'API overview' contains. For an agent, this may be insufficient for understanding the output.
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?
There are zero parameters, and schema description coverage is 100% by default. The description does not add parameter details, but it is not needed. Baseline 4 for no parameters.
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 'Fetch TokenLab's agent-readable API overview' clearly uses a specific verb ('Fetch') and resource ('TokenLab API overview'). The title reinforces this. No sibling tool has a similar name or purpose, so it is well-distinguished.
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 or any prerequisites. While the tool is simple, there is no context about its typical usage (e.g., before making other API calls).
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?
Annotations already indicate this is a write operation that is not read-only, not idempotent, and not destructive. The description adds the availability nuance for images, but does not disclose other behaviors such as rate limits, storage of embeddings, or return format. Given annotations, the description provides marginal additional 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 with no redundant or filler content. The first sentence states the purpose, the second adds a critical availability detail. Every word serves a 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?
The description lacks essential context: there is no mention of return values (e.g., embedding vectors or IDs), error handling, or usage limitations. Since there is no output schema, the description should explain what the tool returns. The tool is moderately complex with 3 parameters, and the description fails to cover these aspects.
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%, which is below the 80% threshold, so the description should compensate. The description adds the availability nuance for text vs image input, but does not elaborate on other parameter constraints or formats. The schema already describes most parameter details, so the description provides minimal added meaning.
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 creates embeddings for multimodal input items, which distinguishes it from the sibling tool create_embedding (presumably text-only). The verb 'create' and resource 'multimodal embeddings' are 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 Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides context on when to use text vs image input, noting potential feature enablement requirements for images. However, it lacks explicit comparisons to sibling tools like create_embedding or guidance on when not to use this tool.
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?
Annotations already indicate non-read-only, open-world, non-idempotent, non-destructive. Description adds context about unknown field forwarding and model availability, but no additional behavioral traits like cost or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two dense sentences with redundant title repetition. Information is valuable but could be more structured and concise.
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?
Given high schema coverage and annotations, description covers key behavioral aspects. Lacks overview of typical use cases vs. siblings, but sufficient for tool 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 95%, so baseline 3 applies. Description does not add parameter-level information beyond what the schema provides.
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 it creates a response using the OpenAI Responses API, distinguishing it from siblings like create_chat_completion by specifying the required model advertisement and route availability.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear conditions for use (model must advertise Responses format, same-protocol route available) and describes best-effort forwarding of unknown fields, but does not explicitly state when not to use or name alternatives.
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?
Annotations already declare the tool as readOnly, openWorld, idempotent, and not destructive. The description adds minimal context ('pricing-only detail' and usage hint). It does not contradict annotations, but the annotations carry the main behavioral burden. The description adds some value but not much beyond what's already structured.
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, no fluff. The first sentence clearly states the action, and the second adds usage guidance. Every sentence is meaningful and front-loaded.
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?
Given the tool's simplicity (single parameter, no output schema), the description provides adequate context. It explains the purpose and usage boundaries. Annotations cover behavioral traits. A minor gap is that it doesn't describe the return format or example output, but that may be inferred from 'pricing-only detail'. Overall, it is complete enough for an agent to use 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?
The input schema has one parameter 'model' with a brief description 'The model ID'. Schema description coverage is 100%. The tool description does not add any additional semantics or formatting details for the parameter, so it neither harms nor improves beyond the schema. Baseline 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 tool retrieves pricing-only detail for one model. It specifies 'pricing-only detail' and distinguishes its use for price explanation, not for non-chat request construction. However, it does not explicitly differentiate from the sibling tool 'get_pricing', which could cause confusion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage guidance: 'Use this endpoint for price explanation, not for non-chat request construction.' It tells when to use (price explanation) and when not to (non-chat request construction), but it does not mention alternatives or compare to other pricing tools.
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?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. Description adds context on what data is returned but does not disclose additional behavioral traits beyond annotations.
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?
Two sentences, front-loaded with core purpose. Efficient but could be slightly more concise by removing redundancy (e.g., 'Get model' in title already says purpose).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no output schema, description covers expected return content (operations, params, endpoints, limits, pricing) and includes a usage directive. Completely adequate.
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 coverage is 100%: single parameter 'model' described as 'The model ID'. Description does not add extra meaning or constraints beyond 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?
Clearly specifies the action ('get') and resource ('model details'), and lists specific content (operations, params, endpoints, limits, pricing). Distinguishes from sibling tools like get_model_pricing and list_models.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit direction: 'Non-chat integrations should read these... before creating a request.' Implies use-case but doesn't explicitly exclude alternatives like get_model_pricing for pricing-only queries.
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?
Discloses authentication method and support for vision/streaming beyond annotations. However, does not describe response format, side effects of repeated calls, or rate limits, which would add further value.
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 sentences covering purpose, capabilities, and authentication with no redundancy. Every sentence adds value, making it highly efficient.
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?
Adequate for a complex tool with 14 parameters and no output schema, but missing details on response format, error handling, and how to construct nested inputs like messages and tools. More context would improve completeness.
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 71% schema coverage, descriptions cover most parameters. The description adds context about vision and streaming, hinting at relevant parameters, but does not explain all complex nested object structures 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?
Clearly states it creates an Anthropic-compatible message using native format. Differentiates from siblings like create_chat_completion (OpenAI) and create_gemini_content by specifying Anthropic's format and Claude models.
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?
Implies usage for Anthropic models with vision and streaming, but does not explicitly state when to use this tool over alternatives or provide exclusions. Lacks explicit 'when-not-to-use' guidance.
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?
Annotations already declare readOnlyHint=true and idempotentHint=true, clearly indicating a safe read operation. The description adds behavioral context about view defaults and recommended_for usage, enhancing transparency beyond annotations.
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 relatively short and front-loaded with the core purpose. It could be slightly more structured, but it contains no filler and every sentence adds value.
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?
Without an output schema, the description gives a high-level idea of the output (list, OpenAI-compatible shape) but does not detail the structure or fields returned. For a list tool, this is acceptable but leaves some ambiguity.
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?
All 5 parameters are well-described in the schema (100% coverage). The description adds extra meaning for view (explains defaults and use cases) and recommended_for (non-chat recommendations), providing value beyond 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 tool lists available models, and distinguishes between views for model selection vs. full OpenAI-compatible shape. It differentiates from siblings like get_model and create tools by focusing on listing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance on when to use view=compact vs view=full, and mentions non-chat recommendations via recommended_for. However, it does not explicitly contrast with other list-like tools (e.g., list_files) or specify when not to use.
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?
Beyond annotations (readOnlyHint=false, destructiveHint=false), the description reveals that the MCP boundary corrects recognized image payloads and rejects unrecognized generic binary before billing, adding valuable behavioral insight not captured in 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 three sentences with front-loaded purpose and no redundant information. Every sentence contributes essential guidance.
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?
While the tool is complex (28 parameters, nested objects, no output schema), the description focuses narrowly on image handling. It lacks guidance on streaming, error handling, or expected return values, leaving gaps for an AI agent.
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 100% schema description coverage, the baseline is 3. The description adds value by specifying inline image handling for the messages parameter, a critical detail beyond the schema's generic descriptions.
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 'Create an OpenAI-compatible chat completion' with a specific verb and resource. It also provides precise handling instructions for image data URLs, distinguishing it from sibling tools like 'create_anthropic_message' or 'create_gemini_content'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context for when to use this tool (chat completions with OpenAI-compatible models) and specific guidance on image MIME types. However, it does not explicitly state when not to use it or mention alternatives.
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?
Annotations indicate a write operation (readOnlyHint=false) with no destructiveness. The description adds authentication requirements, preservation of field naming styles (lowerCamelCase and snake_case), and best-effort forwarding of unknown fields. It does not detail rate limits or idempotency.
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 efficient, front-loading purpose and conditions, but could be more structured with bullet points for readability. No superfluous sentences.
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?
Given the complexity (13 parameters, nested objects) and absence of output schema, the description covers key contextual details: route availability, naming conventions, authentication, and forwarding behavior. It lacks provider-specific error handling or rate limit information, but remains fairly complete.
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 coverage is 77% (high), so the description does not need to redocument parameters. It adds value by explaining authentication parameters (key, headers), naming convention duality, and provider-specific support for unknown fields.
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 title and description clearly state this tool generates content using the Gemini GenerateContent shape, distinguishing it from siblings like create_chat_completion and create_anthropic_message which serve different model families.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description specifies the route is exposed only when model details advertise Gemini requests and a same-protocol route is available, setting a clear precondition. It also explains naming conventions and authentication methods, though it lacks explicit exclusions or alternatives.
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?
Annotations already cover readOnly, idempotent, and non-destructive nature. Description adds context about two access methods (poll_url vs canonical), which is behavioral information beyond what annotations provide.
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?
Two concise sentences: first defines purpose, second provides usage guidance. No wasted words, information is front-loaded.
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 simple tool with one parameter and rich annotations, the description sufficiently covers purpose and ID sources. However, it omits any mention of the return format, which could be helpful since no output schema is provided.
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 coverage is 100%, but description adds meaning by explaining the parameter can come from `id`, `task_id`, or be embedded in `poll_url`. This aids the agent in correctly obtaining the ID.
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 retrieves status and result of an async generation task. It specifies the verb 'get', the resource 'async task status', and distinguishes from other purposes by mentioning polling vs canonical endpoint.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance to prefer the `poll_url` returned by the create response, and contrasts with the canonical `/v1/tasks/{id}` endpoint. This helps the agent choose between alternatives, though it doesn't explicitly mention when not to use the tool.
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?
Annotations indicate destructive mutation (readOnlyHint=false, destructiveHint=true). Description adds specific context on cancellation conditions and supported tasks. No contradictions.
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?
Two sentences with no waste. First sentence states primary action, second provides necessary constraints. Front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple cancel operation with one parameter and no output schema, the description covers all necessary behavioral details, including restrictions and supported task types.
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 describes the single parameter 'id' with full coverage. Description adds no extra parameter details beyond what schema provides. Baseline score of 3 applies.
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?
Clearly states the tool cancels a queued async task, specifies supported task types (Seedance video tasks) and states. Distinguishes from siblings as no other cancel tool exists.
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?
Explicitly describes when to use (queued tasks with cancellation support) and when not (already processing, completed, failed, expired, unsupported). Provides concrete examples of supported task IDs.
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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