Enkrypt AI MCP Server

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation2/5
Multiple tools have overlapping purposes that could cause confusion. For example, add_redteam_task, add_agent_redteam_task, and add_custom_redteam_task all create red team tasks with subtle differences that may not be clear from their names alone. Similarly, add_model and add_model_from_url both add models but with different configurations, and guardrails_detect vs use_policy_to_detect both perform detection but with different approaches. The descriptions help, but the boundaries are unclear.
Naming Consistency4/5
Most tools follow a consistent verb_noun pattern (e.g., add_model, get_model_details, list_models, remove_model), which is clear and predictable. However, there are minor deviations like guardrails_detect (noun_verb) and use_policy_to_detect (verb_noun_preposition_noun), which break the pattern slightly but are still readable. Overall, the naming is mostly consistent.
Tool Count2/5
With 28 tools, the count feels excessive for the apparent scope of AI model and red teaming management. Many tools could be consolidated (e.g., multiple red team task additions, redundant detection tools), leading to a bloated interface. A well-scoped server for this domain would typically have 10-15 tools, making this set heavy and potentially overwhelming for agents.
Completeness4/5
The tool set covers a broad range of operations for managing AI models, red teaming tasks, deployments, and guardrails policies, including CRUD actions and workflow steps like hardening system prompts. However, there are minor gaps, such as no tools for updating or deleting red team tasks directly, and some operations rely on combinations of tools that might be less intuitive. Overall, the surface is largely complete for the domain.
Average 3.1/5 across 28 of 28 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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This repository is licensed under Apache 2.0.
This repository includes a README.md file.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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

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 provides minimal behavioral information. It states this is a removal operation (implying destructive action) and mentions the return format, but doesn't disclose important behavioral traits like whether removal is permanent, what permissions are required, whether it affects associated resources, or any rate limits. The description doesn't contradict annotations since none exist, but it's insufficient 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with three brief sections (purpose, args, returns). Each sentence serves a clear purpose with no wasted words. The structure is logical and front-loaded with the main action. It could be slightly more informative while maintaining conciseness.
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 operation with no annotations, no output schema, and minimal parameter documentation, the description is incomplete. It doesn't explain what 'removing a model' entails operationally, what happens to dependent resources, whether the action is reversible, or what the response dictionary contains. Given the complexity of model management and the presence of sibling tools like 'modify_model_config', 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% (parameter has no description in schema), and the description only states 'The saved name of the model to be removed' without explaining what a 'saved name' is, how it differs from other identifiers, or where to find this information. For a single parameter with zero schema documentation, this minimal explanation doesn't adequately compensate for the 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 'Remove a model' which is a clear verb+resource combination, but it doesn't differentiate from sibling tools like 'remove_deployment' or 'remove_guardrails_policy' that follow the same pattern. The purpose is understandable but lacks specificity about what type of model is being removed or how it differs from other removal 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 about when to use this tool versus alternatives. There's no mention of prerequisites, what happens after removal, or when not to use it. The description doesn't reference sibling tools like 'list_models' that might be needed first or 'add_model' as a complementary operation.
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 a read operation ('Get'), which implies non-destructive behavior, but doesn't address permissions, rate limits, error handling, or response format details. The description adds some context by suggesting mitigation actions, but this is advisory and doesn't clarify the tool's own behavior or constraints, leaving 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, but includes extraneous content: the mitigation suggestions are not part of the tool's functionality and clutter the description. The structure with 'Args:' and 'Returns:' sections is clear, but the mitigation advice adds unnecessary length and distracts from the tool's actual behavior, reducing overall efficiency.
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 redteam tasks and the lack of annotations and output schema, the description is incomplete. It doesn't explain what a 'results summary' entails, how to interpret the returned dictionary, or any error cases. The mitigation suggestions are out of scope and don't address the tool's operational context, leaving the agent with insufficient information to use the tool effectively in a security testing environment.
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 0% description coverage, with one parameter 'test_name' documented only by its title. The description adds minimal semantics by stating 'The name of the redteam test,' but this is basic and doesn't elaborate on format, examples, or constraints. For a single parameter with no schema documentation, the description should compensate more to clarify usage, but it provides only a vague definition, insufficient for full 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: 'Get the results summary of a redteam task.' It specifies the verb ('Get') and resource ('results summary of a redteam task'), making the action explicit. However, it doesn't differentiate from sibling tools like 'get_redteam_task_details' or 'get_redteam_task_status', which might retrieve different aspects of redteam tasks, 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. It doesn't mention sibling tools like 'get_redteam_task_details' or 'get_redteam_task_status', nor does it specify prerequisites or contexts for usage. The only implied usage is after a redteam task has been run, but this is not explicitly stated, leaving the agent without clear direction on 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. While it mentions the tool 'Adds' something (implying creation/mutation), it doesn't describe what happens after adding - whether the task executes immediately, gets queued, requires approval, or has side effects. The example config includes detailed structure but doesn't explain behavioral traits like error handling, permissions needed, or rate limits. Significant behavioral gaps remain.
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 excessively long and poorly structured - the massive JSON example dominates the text. While the initial sentence is concise, the example usage with embedded comments and repeated 'ask user for this' text creates significant noise. The description is front-loaded but then becomes bloated with implementation details that belong elsewhere. Many sentences don't earn their place in a tool description.
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 3 parameters with 0% schema coverage, no annotations, no output schema, and complex nested objects, the description provides substantial parameter guidance but lacks other critical context. It doesn't explain return values (only mentions 'A dictionary containing the response message and details'), doesn't cover error conditions, and omits behavioral expectations. The parameter coverage is strong, but overall completeness is only adequate for this complex tool.
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% schema description coverage and 3 parameters, the description provides substantial value beyond the bare schema. It explains that 'agent_saved_name' refers to 'the saved name of the agent', 'agent_version' is 'the version of the agent', and 'agent_redteam_model_config' is 'the configuration for the redteam task' with extensive example structure and guidance to 'ASK USER FOR ALL THESE DETAILS'. This compensates well for the schema's lack of descriptions.
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 'Add a redteam task using a saved agent' which provides a basic verb+resource combination, but it's vague about what 'add' means operationally (creates, schedules, executes?) and doesn't clearly differentiate from sibling tools like 'add_redteam_task' or 'add_custom_redteam_task'. The purpose is understandable 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 explicit guidance on when to use this tool versus alternatives like 'add_redteam_task' or 'add_custom_redteam_task' is provided. The description mentions using a 'saved agent' which implies a prerequisite, but doesn't explain when this specific tool is appropriate versus other redteam task creation methods. Usage context is implied but not clearly 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 the full burden. It mentions the tool lists all deployments and prints details of the first, but does not disclose behavioral traits such as pagination, rate limits, permissions required, or what 'print details' entails. This leaves significant gaps for a tool that likely interacts with deployments.
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 with two sentences, but the second sentence about printing details of the first deployment adds potential confusion without clear value. It could be more front-loaded by focusing solely on the listing function. Some waste is present.
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 list operation with potential complexity (e.g., multiple deployments), the description is incomplete. It does not explain the return format beyond 'a dictionary containing the list of deployments,' missing details like structure, keys, or error handling. More context is needed for effective use.
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 0 parameters with 100% coverage, so no parameter information is needed. The description does not add parameter details, which is appropriate. Baseline is 4 for 0 parameters, as the schema fully covers the absence of inputs.
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 'List all deployments and print details of the first deployment,' which provides a clear verb ('List') and resource ('deployments'). However, it does not differentiate from sibling tools like 'get_deployment_details' or explain how it differs from other listing tools (e.g., 'list_models'). The purpose is clear but 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?
The description provides no guidance on when to use this tool versus alternatives. It does not mention when to use 'list_deployments' over 'get_deployment_details' or other sibling tools, nor does it specify any prerequisites or exclusions. Usage is implied but not explicit.
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 returning a dictionary but doesn't disclose format, pagination, rate limits, permissions needed, or what 'print details' entails operationally. This is inadequate 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief but inefficiently structured. The first sentence mixes listing and printing actions, and the 'Returns' section is redundant since it restates the obvious. It could be more front-loaded and concise 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?
Given no annotations, no output schema, and a description that lacks detail on behavior, format, or differentiation from siblings, this is incomplete. For a list operation among many sibling tools, more context on what 'models' entails and the return structure is needed.
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 doesn't add parameter semantics, but this is acceptable given the lack of parameters, aligning with the baseline expectation for zero-param tools.
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 'List all models' which provides a clear verb+resource, but it's vague about what 'print details of the first model' means in the context of returning a dictionary. It doesn't distinguish from siblings like 'get_model_details' or 'list_deployments', leaving ambiguity about scope 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 is provided on when to use this tool versus alternatives like 'get_model_details' or 'add_model'. The description implies a listing function but doesn't specify 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.
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 'modify' and 'update' imply mutation, it doesn't specify permissions required, whether changes are reversible, rate limits, error conditions, or what 'update the deployment' entails operationally. The Returns section mentions a response dictionary but gives no details about success/failure indicators or structure. 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is structured with Args, Example Usage, and Returns sections, which is good organization. However, the example is overly long (25+ lines) with embedded comments and syntax issues, making it inefficient. The core purpose statement is clear but could be more front-loaded. Some sentences like 'Add these if any additional detectors than that in the policy are needed' are instructional rather than descriptive.
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 2 parameters (one being a complex nested object), 0% schema description coverage, no annotations, and no output schema, the description is incomplete. While it shows an example configuration, it doesn't explain the full behavioral context: what fields are modifiable, validation rules, side effects, error handling, or response format. The Returns section is vague ('A dictionary containing...') without structural details.
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 0%, so the description must compensate. It names both parameters ('deployment_name' and 'new_deployment_config') and provides an extensive example showing a complex nested configuration object with guardrails policies. However, the example contains syntax errors (e.g., 'sample_deployment_config =' inside JSON) and doesn't clearly map to the actual parameter structure. It adds some semantic value but doesn't fully document the parameter expectations beyond the basic names.
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: 'Modify the deployment configuration and update the deployment.' This specifies the verb ('modify' and 'update') and resource ('deployment configuration' and 'deployment'), making it clear this is a mutation operation. However, it doesn't explicitly differentiate from sibling tools like 'modify_model_config' or 'update_guardrails_policy' that might handle similar configuration changes.
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 'add_deployment', 'get_deployment_details', 'list_deployments', and 'remove_deployment', there's no indication of prerequisites (e.g., deployment must exist), when to choose modification over recreation, or what happens if the deployment doesn't exist. The example shows complex configuration but doesn't explain 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?
With no annotations provided, the description carries the full burden. It discloses that the tool creates a new deployment (a write operation) and mentions behavioral aspects like asking users about blocking detectors and referencing another tool for policy info. However, it doesn't cover critical details like required permissions, rate limits, idempotency, or error handling, leaving 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.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is overly verbose and poorly structured. The example usage is embedded with comments and incomplete syntax (e.g., 'sample_deployment_config =' inside the JSON), making it confusing. Key information is buried in comments rather than front-loaded. Sentences like 'Always ask user...' are important but could be 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 complexity (1 parameter with nested objects, no output schema, no annotations), the description is insufficient. It provides an example but lacks a clear explanation of the deployment concept, expected return values (only vaguely mentions 'response message and details'), error cases, or dependencies. For a tool that likely involves significant configuration, this leaves too much ambiguity.
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 provides an example with nested configuration details (name, model_saved_name, guardrails policies), which adds meaningful context beyond the generic 'object' type in the schema. However, it doesn't explain all possible fields or constraints (e.g., required vs. optional, data types, validation rules), leaving the parameter documentation incomplete.
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: 'Add a new deployment using the provided configuration.' It specifies the verb ('Add') and resource ('deployment'), distinguishing it from siblings like 'modify_deployment_config' or 'remove_deployment'. However, it doesn't explicitly differentiate from 'add_agent' or 'add_model' in terms of what type of resource it creates.
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 includes implicit guidance: 'Always ask user if they want to block any of the detectors in the policy for both input and output' and references the 'get_guardrails_policy' tool for policy details. However, it lacks explicit when-to-use vs. alternatives (e.g., when to use 'add_deployment' vs. 'modify_deployment_config' or prerequisites). The guidance is more about implementation details than 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 the full burden of behavioral disclosure. It only states the action ('Get all guardrails policies') without mentioning permissions, rate limits, pagination, or response format. This is inadequate for a tool with zero annotation coverage, as critical behavioral traits are omitted.
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 appropriately sized for the tool's simplicity, 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 does not explain what 'guardrails policies' are, how results are returned, or any behavioral aspects like safety or performance. For a tool with no structured data support, 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.
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 information is needed. The description does not add semantics beyond the schema, but this is acceptable given the lack of parameters, aligning with the baseline of 4 for zero 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 'Get all guardrails policies' clearly states the verb ('Get') and resource ('guardrails policies'), making the purpose understandable. However, it lacks specificity about what 'all' entails (e.g., all in a system, all for a user) and does not distinguish it from sibling tools like 'retrieve_policy_configuration' or 'guardrails_detect', leaving ambiguity in 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. With sibling tools such as 'retrieve_policy_configuration' and 'guardrails_detect', the description fails to specify contexts, prerequisites, or exclusions, offering no help in 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 for behavioral disclosure. While 'retrieve' implies a read-only operation, it doesn't specify authentication requirements, rate limits, error conditions, or what happens if the model doesn't exist. The mention of 'redteam task' in the parameter description adds some context but doesn't fully describe 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with a clear structure: purpose statement followed by parameter and return value sections. Every sentence serves a purpose, though the parameter descriptions could be more efficiently integrated. The formatting with 'Args:' and 'Returns:' sections is helpful for readability.
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 2 parameters, 0% schema description coverage, no annotations, and no output schema, the description is incomplete. While it covers the basic purpose and parameters, it lacks information about authentication, error handling, return format details, and differentiation from sibling tools. The mention of 'redteam task' provides some context but doesn't fully address the tool's operational requirements.
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 0%, so the description must compensate. It provides basic semantic meaning for both parameters: 'model_saved_name' is 'the name under which the model is saved' and 'model_version' is 'the version of the model to be used for the redteam task.' This adds value beyond the bare schema, but doesn't explain format constraints, examples, or how these parameters interact.
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: 'Retrieve details of a specific model using its saved name.' This specifies the verb ('retrieve details') and resource ('specific model'), making the intent unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'list_models' or 'get_deployment_details', which would be needed 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 minimal usage guidance. It mentions 'using its saved name' and references 'the redteam task' in the parameter description, but doesn't explain when to use this tool versus alternatives like 'list_models' or 'get_deployment_details'. There's no explicit guidance on prerequisites, 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves details but doesn't specify permissions needed, rate limits, error handling, or what happens if the test_name doesn't exist. For a read operation 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with the core purpose stated first followed by brief sections for args and returns. Every sentence earns its place, though the structure is simple and could be more polished.
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 retrieving task details, no annotations, no output schema, and low schema coverage, the description is incomplete. It mentions the return includes 'the system prompt of the target model used', but doesn't fully describe the dictionary structure or other possible fields, leaving gaps for effective tool 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 adds minimal semantics beyond the input schema. It documents the single parameter 'test_name' and its purpose, but with 0% schema description coverage, it doesn't fully compensate by explaining format constraints or examples. The baseline is 3 since it provides some value, but more detail would be helpful given the low 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 verb 'retrieve' and the resource 'details of a redteam task', making the purpose specific. However, it doesn't explicitly differentiate from sibling tools like 'get_redteam_task_results_summary' or 'get_redteam_task_status', which could retrieve related but different information about redteam tasks.
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 siblings like 'get_redteam_task_results_summary' and 'get_redteam_task_status' that might retrieve overlapping or complementary data, the description lacks context for choosing between them, offering no explicit when/when-not instructions 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?
With no annotations provided, the description carries full burden but offers minimal behavioral insight. It mentions returning a dictionary but doesn't disclose what the status includes (e.g., running, completed, failed), whether it's read-only, if authentication is needed, or any rate limits. This leaves significant gaps for a tool that likely interacts with sensitive redteam tasks.
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, followed by structured Args and Returns sections. Every sentence earns its place with no wasted words, making it efficiently sized and easy to parse for an AI agent.
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 redteam tasks, no annotations, no output schema, and low schema coverage, the description is incomplete. It lacks details on status values, error handling, permissions, or how it integrates with sibling tools like 'add_redteam_task', making it inadequate for safe and effective use in this 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 0%, so the description must compensate. It adds that 'test_name' is 'The name of the redteam test', providing basic semantics beyond the schema's title 'Test Name'. However, it doesn't clarify format, examples, or constraints (e.g., case sensitivity, allowed characters), leaving the parameter partially documented.
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 ('status of a redteam task'), making the purpose specific and understandable. It distinguishes from siblings like 'get_redteam_task_details' and 'get_redteam_task_results_summary' by focusing specifically on status rather than details or results, though the distinction could be more explicit.
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 'get_redteam_task_details' or 'list_redteam_tasks'. The description only states what it does without context about prerequisites, timing, or comparisons to sibling tools, leaving the agent to infer 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a removal/deletion operation (implying mutation), but doesn't specify if it's reversible, requires specific permissions, has side effects, or details error conditions. The return format is vaguely described as 'a dictionary containing the response message and details', lacking concrete behavioral traits.
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 with the core purpose in the first sentence. The Args and Returns sections are structured but slightly verbose (e.g., 'A dictionary containing...' could be tighter). Overall, it's efficient with minimal waste.
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 critical context: no warnings about irreversibility, no permission requirements, no error handling details, and only a vague return description. Given the complexity of policy removal, this leaves significant gaps for an 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 0%, so the description must compensate. It documents the single parameter 'policy_name' and its purpose ('The name of the policy to remove'), adding essential semantics beyond the bare schema. However, it doesn't clarify format constraints (e.g., case-sensitivity, allowed characters) or provide examples, leaving some ambiguity.
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 ('Remove') and resource ('an existing guardrails policy'), making the purpose unambiguous. It distinguishes from siblings like 'add_guardrails_policy' and 'update_guardrails_policy' by specifying removal, though it doesn't explicitly contrast with 'mitigation_guardrails_policy' or 'retrieve_policy_configuration'.
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 existing policy), exclusions, or compare with related tools like 'update_guardrails_policy' or 'mitigation_guardrails_policy', 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 mentions detection but doesn't disclose behavioral traits like what constitutes a 'violation', whether this is a read-only analysis or has side effects, authentication requirements, rate limits, or error conditions. The description is too vague about the actual behavior beyond the basic operation.
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 with a clear purpose statement followed by structured sections for Args and Returns. Every sentence serves a purpose, though the return description could be more specific. The structure helps readability but isn't perfectly front-loaded with all critical 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 no annotations, 0% schema coverage, no output schema, and 2 parameters, the description is incomplete. It lacks details on policy behavior, detection criteria, output format specifics, error handling, and differentiation from sibling tools. For a detection tool with policy dependencies, this leaves too many contextual gaps for reliable 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 0%, so the schema provides no parameter documentation. The description adds basic semantics for both parameters ('policy_name' and 'text'), explaining what they represent. However, it doesn't provide details like policy format, text length limits, or examples, leaving significant gaps in understanding how to use them 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 tool's purpose: 'Use a policy to detect violations in the provided text.' This specifies the verb ('detect'), resource ('violations'), and target ('text'). However, it doesn't explicitly differentiate from sibling tools like 'guardrails_detect' or 'retrieve_policy_configuration', which appear related to similar policy/detection 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. With sibling tools like 'guardrails_detect' and 'retrieve_policy_configuration' present, there's no indication of the specific use case, prerequisites, or how this tool differs from others in the policy/detection domain.
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 a removal operation, implying mutation/destruction, but doesn't disclose critical behavioral traits: whether this is irreversible, requires specific permissions, has side effects (e.g., stopping services), or provides confirmation prompts. 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.
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 structured Args/Returns sections. Every sentence earns its place, though the Returns section could be more specific. No redundant information is present.
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 this is a destructive mutation tool with no annotations and no output schema, the description is minimally adequate but incomplete. It covers the basic action and parameter, but lacks critical context: no warnings about irreversible deletion, no error conditions, no details on return structure beyond 'dictionary containing...', and no guidance on usage versus siblings.
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% schema description coverage and only 1 parameter, the description compensates well by explaining 'deployment_name' as 'The name of the deployment to remove.' This adds clear semantic meaning beyond the schema's basic string type. However, it doesn't specify format constraints (e.g., case sensitivity, allowed characters) or provide 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 verb ('Remove') and resource ('an existing deployment'), making the purpose immediately understandable. It distinguishes from siblings like 'list_deployments' and 'get_deployment_details' by specifying a destructive action. However, it doesn't explicitly differentiate from other removal tools like 'remove_model' or 'remove_guardrails_policy' beyond the resource name.
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., deployment must exist), when not to use it (e.g., if deployment is active), or direct alternatives among siblings like 'modify_deployment_config' for updates. The agent must 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. It mentions 'retrieve and print' but doesn't specify whether this is a read-only operation, what permissions are required, how errors are handled, or if there are rate limits. 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 well-structured and front-loaded with the core purpose, followed by brief sections for arguments and returns. Every sentence earns its place without redundancy, 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.
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 (one parameter, no output schema, no annotations), the description is minimally complete. It covers the basic purpose and parameter but lacks details on behavior, error handling, and differentiation from siblings, which are important for full contextual 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?
The description adds minimal semantics beyond the input schema: it explains that 'policy_name' is 'the name of the policy to retrieve,' which clarifies the parameter's purpose. However, with 0% schema description coverage and only one parameter, this is adequate but not comprehensive—it doesn't detail format constraints or 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 tool's purpose with a specific verb ('retrieve and print') and resource ('policy configuration'), making it immediately understandable. However, it doesn't differentiate from sibling tools like 'get_guardrails_policy' or 'use_policy_to_detect', which appear to be related to policy operations, so it doesn't fully distinguish itself from 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 sibling tools like 'get_guardrails_policy' and 'use_policy_to_detect' that might overlap in functionality, there's no indication of context, prerequisites, or exclusions for using this specific retrieval 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?
With no annotations provided, the description carries full burden for behavioral disclosure. It describes the action ('Add a new guardrails policy') and return format, but lacks critical behavioral details such as permission requirements, whether this is a mutating operation, error conditions, or system impacts. The example usage helps but doesn't cover behavioral traits comprehensively.
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 appropriately front-loaded with the core purpose, but becomes verbose with extensive detector examples. While the examples are helpful, they could be more efficiently structured. Some sentences (like the detailed detector list) could be condensed without losing 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 complexity (3 parameters with nested objects, no annotations, no output schema), the description does a reasonable job but has gaps. It covers parameters well and mentions the return format, but lacks behavioral context and doesn't explain the 'policy_description' parameter. For a tool that creates policies, more context about system impact would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description compensates exceptionally well by providing detailed parameter semantics. It explains 'policy_name' and 'detectors' thoroughly, including available detector types, configuration models, and comprehensive examples. This adds significant value 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 verb 'Add' and the resource 'guardrails policy', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'update_guardrails_policy' or 'mitigation_guardrails_policy', which would be needed 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?
No guidance is provided on when to use this tool versus alternatives like 'update_guardrails_policy' or 'remove_guardrails_policy'. The description only explains what the tool does, not when it should be selected over other tools in the context of the sibling list.
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 adds a model and returns a response dictionary, but lacks critical behavioral details: whether this is a write operation (implied but not stated), what permissions are needed, if there are rate limits, whether the model becomes immediately available, or what happens on failure. The description adds basic context but 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized but could be more front-loaded. The core purpose is stated first, but the example usage is lengthy and includes redundant comments (e.g., 'Always fixed to...'). The 'Ask the user...' instruction is useful but placed after the example. Some sentences could be trimmed for efficiency, though overall structure is logical.
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 (1 parameter with nested objects, no annotations, no output schema), the description is partially complete. It excels in parameter documentation but lacks behavioral context for a mutation tool. Without annotations or output schema, it should explain more about the operation's effects, error handling, or return value specifics beyond 'dictionary containing...' to be fully adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must fully compensate. It provides extensive parameter semantics: it explains the 'config' parameter is a dictionary with a detailed example structure, including all nested fields, their purposes, and fixed values. This adds significant meaning beyond the minimal schema, effectively documenting the single parameter's expected format and content.
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: 'Add a new model using the provided configuration.' This specifies the verb ('Add') and resource ('new model'), and distinguishes it from siblings like 'add_model' (which likely adds models from other sources) by specifying 'from url' in the tool name. However, it doesn't explicitly differentiate from 'add_model' in the description text itself.
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 includes 'Ask the user for the url before passing the config to the tool,' which gives some procedural context, but doesn't explain when to use this tool versus alternatives like 'add_model' or other model-related tools. No explicit when/when-not guidance or prerequisites 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 full burden for behavioral disclosure. While it states the tool modifies and updates a model, it doesn't describe important behavioral aspects: whether this requires special permissions, if changes are reversible, what happens to existing configurations not mentioned, or potential side effects. The example shows authentication details (apikey), suggesting this might involve privileged operations, but this isn't explicitly stated.
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 front-loaded with the core purpose, but the extensive example occupies significant space. While the example is valuable for parameter clarification, it makes the description quite long. The structure with Args/Returns sections is clear, but some information in the example (like 'Always LLM') could potentially be moved to parameter documentation.
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 mutation tool with no annotations and no output schema, the description provides good parameter documentation but lacks important context. It doesn't explain what 'update the model' means operationally, what the return dictionary contains, or potential error conditions. The example helps but doesn't fully compensate for the missing behavioral and output context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by providing detailed parameter semantics. It explains both parameters thoroughly: 'new_model_config' gets an extensive example with field-by-field documentation, and 'test_model_saved_name' is clearly defined as 'The saved name of the model to be tested.' This goes well beyond what the bare schema provides.
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: 'Modify the model configuration and update the model.' This specifies both the action (modify/update) and the resource (model configuration/model). However, it doesn't explicitly differentiate from sibling tools like 'modify_deployment_config' or 'update_guardrails_policy', 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 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 'add_model', 'get_model_details', 'list_models', and 'remove_model' available, there's no indication of when this modification tool is appropriate versus creating a new model or retrieving existing details.
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 offers limited behavioral insight. It implies a mutation operation ('update') but doesn't disclose permissions needed, whether changes are reversible, rate limits, or error handling. The return format is vaguely described as 'dictionary containing response message and updated policy details', lacking specifics on structure or success/failure indicators.
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 front-loaded with the core purpose but becomes lengthy with detailed detector examples. While informative, the extensive listing of detectors could be more concise. Structure is logical with Args and Returns sections, but some redundancy in examples slightly reduces efficiency.
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 3 parameters, nested objects, no annotations, and no output schema, the description is partially complete. It excels in parameter semantics but lacks behavioral context, usage guidelines, and detailed return value explanation. For a mutation tool with complexity, it should cover more aspects like error cases or side effects to be fully adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description compensates fully. It details 'policy_name' as the target policy and 'detectors' as a dictionary with specific detector configurations, including examples for each detector type (e.g., injection_attack, pii) and a usage example. This adds significant meaning beyond the bare schema, clarifying parameter purposes and formats 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 verb 'update' and resource 'existing guardrails policy with new configuration', making the purpose unambiguous. It distinguishes from siblings like 'add_guardrails_policy' (create vs update) and 'remove_guardrails_policy' (modify vs delete), though not explicitly named. The specificity is good 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?
The description provides no guidance on when to use this tool versus alternatives like 'add_guardrails_policy' for creation or 'modify_deployment_config' for related changes. It mentions updating an existing policy but doesn't specify prerequisites (e.g., policy must exist) or contextual constraints, leaving usage unclear relative to 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?
With no annotations provided, the description carries full burden for behavioral disclosure. While it mentions that the tool 'Adds a new agent' (implying a write/mutation operation), it doesn't address critical behavioral aspects: whether this requires specific permissions, what happens on failure, whether the agent becomes immediately active, or if there are rate limits. The example config provides some implementation details but lacks 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately front-loaded with the core purpose, but the extensive example and implementation notes create significant length. While the content is valuable given the complex parameter, some information (like repeated 'Mandatory' labels and implementation directives) could be more efficiently structured. The 'NOTE' section feels like implementation guidance rather than pure tool description.
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 complex nested parameter structure (1 parameter with deep nesting, 0% schema coverage, no output schema), the description provides substantial context. It thoroughly documents the config parameter's structure, includes an example with field semantics, and specifies mandatory requirements. The main gap is lack of output information (only mentions 'A dictionary containing the response message and details' without structure), but this is partially mitigated by the detailed input documentation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage and a single complex parameter ('config'), the description provides extensive semantic information that completely compensates for the schema gap. It details the nested structure of AgentConfig, provides a comprehensive example with field explanations, specifies mandatory versus optional fields, and includes implementation notes about placeholder usage and required user input collection.
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: 'Add a new agent using the provided configuration.' It specifies the verb ('Add') and resource ('agent'), making the function unambiguous. However, it doesn't explicitly differentiate this from sibling tools like 'add_model' or 'add_deployment', which appear to be related creation 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. While it mentions 'DO NOT ASSUME ANY FIELDS AND ASK THE USER FOR ALL THE DETAILS,' this is about parameter collection rather than tool selection. There's no indication of prerequisites, when this tool is appropriate versus other 'add_' tools, or what scenarios warrant 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 the full burden. It states the tool lists tasks with optional filtering, which covers basic behavior, but lacks critical details such as pagination, rate limits, authentication requirements, error conditions, or whether the operation is read-only or has side effects. For a tool with no annotation coverage, this leaves significant gaps in understanding its operational characteristics.
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 well-structured and front-loaded, starting with the core purpose in the first sentence. The 'Args' and 'Returns' sections are clearly labeled and concise, with no redundant information. Every sentence earns its place by adding value, such as clarifying parameter behavior and return format.
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 no annotations, no output schema, and low schema description coverage, the description provides basic context but is incomplete. It covers the purpose and parameter usage adequately, but lacks details on behavioral traits, error handling, or output structure beyond 'a dictionary containing the list.' For a tool in this context, more comprehensive information would be beneficial 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?
The input schema has 1 parameter with 0% description coverage, so the description must compensate. It explains the 'status' parameter as 'The status to filter tasks by (e.g., "Finished"). If None, list all tasks,' which adds meaningful semantics beyond the schema. However, it doesn't specify possible status values or formatting, leaving some ambiguity. With low schema coverage, this provides moderate but incomplete compensation.
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 ('all redteam tasks'), making the purpose immediately understandable. It distinguishes this tool from siblings like 'get_redteam_task_details' or 'get_redteam_task_status' by focusing on listing multiple tasks rather than retrieving details about specific ones. However, it doesn't explicitly differentiate from other list tools like 'list_deployments' or 'list_models' beyond 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage through the phrase 'optionally filtered by status,' suggesting this tool is for listing tasks with optional filtering. However, it doesn't provide explicit guidance on when to use this versus alternatives like 'get_redteam_task_details' for specific tasks or 'get_redteam_task_status' for status checks. 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?
With no annotations provided, the description carries full burden for behavioral disclosure. It states this is a retrieval operation but doesn't mention whether it requires authentication, has rate limits, what happens if the deployment doesn't exist, or the structure of returned details. For a read operation 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 efficiently structured with a clear purpose statement followed by Args and Returns sections. Every sentence earns its place, with no redundant information. The three-part format (purpose, parameter, return) is well-organized and appropriately sized for a single-parameter tool.
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 single-parameter read operation with no annotations and no output schema, the description provides adequate basic information but lacks important context. It doesn't explain what 'details' include in the return dictionary, error conditions, or how this tool relates to other deployment tools like 'modify_deployment_config' or 'remove_deployment'. The description is minimally viable but has clear gaps.
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 0%, so the description must compensate. It clearly explains the single parameter 'deployment_name' as 'The name of the deployment to retrieve details for', adding meaningful context beyond the schema's basic type information. However, it doesn't specify format constraints or provide examples, preventing a perfect 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 ('Retrieve details') and target resource ('specific deployment using its name'), making the purpose unambiguous. It distinguishes from sibling 'list_deployments' by focusing on a single deployment rather than listing multiple. However, it doesn't specify what 'details' include, 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 implies usage when you need details for a specific deployment (vs. 'list_deployments' for multiple deployments), but doesn't explicitly state when to use this tool versus alternatives like 'get_model_details' or 'get_redteam_task_details'. No exclusions or prerequisites are mentioned, leaving some ambiguity about appropriate contexts.
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 creates a policy but doesn't disclose behavioral traits like required permissions, whether it's idempotent, error handling, or rate limits. The description adds some context about input processing but lacks critical 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized but not optimally structured. Key information is front-loaded, but the note about 'After getting the configuration...' could be integrated more smoothly. Some sentences are lengthy but necessary 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 no annotations, no output schema, and a complex nested parameter, the description is moderately complete. It explains parameter semantics well and references related tools, but lacks details on return values, error conditions, and behavioral constraints that would be needed for full understanding.
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 0%, so the description must compensate. It provides detailed semantics for the single parameter: format requirements, source tool, filtering logic (top 20 categories by success percent), and example structure. This adds substantial 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 tool creates a guardrails policy using redteam results summary, specifying the verb 'create' and resource 'guardrails policy'. It distinguishes from siblings like 'add_guardrails_policy' by indicating this tool processes input first, though not explicitly contrasting all alternatives.
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?
It provides clear context: use when you have redteam results summary from 'get_redteam_task_results_summary' tool, and mentions using 'add_guardrails_policy' afterward. However, it doesn't explicitly state when NOT to use this tool or compare with alternatives like 'update_guardrails_policy'.
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 describes what the tool does (detects sensitive content) and provides configuration details for detectors, but doesn't disclose behavioral traits such as performance characteristics, rate limits, authentication needs, or what happens if no sensitive content is found. The description adds some context but lacks comprehensive 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately front-loaded with the core purpose, but it becomes verbose with detailed detector configurations and examples. While this information is valuable, it could be more structured or concise. Some sentences (like the extensive detector list) might benefit from better organization, but overall, it's not overly redundant.
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 (2 parameters with nested objects, no output schema, no annotations), the description is fairly complete. It covers the purpose, parameters in detail, and return value format. However, it lacks output schema details (e.g., structure of 'dictionary containing detection results'), and without annotations, it misses behavioral context like error handling or side effects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must compensate, and it does so effectively. It explains the 'text' parameter as 'The text to detect sensitive content in' and provides extensive documentation for 'detectors_config', including available detectors, their configurations, and example usage. This adds significant meaning beyond the minimal 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 tool's purpose: 'Detect sensitive content using Guardrails.' It specifies the verb ('detect') and resource ('sensitive content'), though it doesn't explicitly differentiate from sibling tools like 'use_policy_to_detect' or 'mitigation_guardrails_policy', which appear related. The purpose is clear but 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?
The description provides no guidance on when to use this tool versus alternatives like 'use_policy_to_detect' or other sibling tools. It includes an example of how to configure detectors but doesn't explain the context or prerequisites for using this detection method. No explicit when/when-not statements or alternative recommendations are present.
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. It mentions 'harden' which implies a write/mutation operation, but doesn't disclose critical behavioral traits like whether this is destructive, what permissions are required, rate limits, or what 'hardening' entails. 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded with the core purpose. The Args and Returns sections are clearly structured. While efficient, the formatting with markdown-style headers could be slightly more polished for maximum 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 complexity (mutation tool with nested objects, no annotations, no output schema), the description provides good parameter semantics but lacks behavioral context about the hardening process itself. It doesn't explain what 'hardened' means, what changes are made, or what the return dictionary contains. For a mutation tool with no structured safety information, this leaves significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by providing detailed semantic information for both parameters. It specifies the exact format and content requirements for 'redteam_results_summary' (top 20 categories, dictionary structure, success percent focus) and clarifies that 'system_prompt' should come from a specific tool. This adds significant value 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 ('Harden') and the resource ('system prompt'), and specifies the inputs needed. It distinguishes from siblings by focusing on prompt hardening rather than retrieval or management tasks. However, it doesn't explicitly contrast with tools like 'mitigation_guardrails_policy' which might have overlapping security purposes.
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 by specifying that inputs should be retrieved from specific sibling tools ('get_redteam_task_results_summary' and 'get_redteam_task_details'), which provides context for when to use this tool. However, it doesn't explicitly state when NOT to use it or name alternatives for similar hardening tasks, leaving some ambiguity.
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 the full burden of behavioral disclosure. It effectively describes key behaviors: it's a mutation tool (implied by 'Add'), includes important constraints (incompatibility with audio/image modalities), specifies prerequisites (check model availability), and outlines complex configuration requirements. It doesn't cover rate limits or error handling, but provides substantial 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.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is excessively long and poorly structured. While the initial purpose statement is clear, it's followed by a massive embedded example that dominates the text. The information is valuable but not front-loaded efficiently, making it difficult for an agent to quickly parse essential details. The length exceeds what's necessary for concise tool selection.
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 (3 parameters with nested objects, 0% schema coverage, no output schema, no annotations), the description provides substantial context. It explains the tool's purpose, constraints, prerequisites, and detailed parameter semantics. The main gap is the lack of output description (returns 'a dictionary' without specifics), but otherwise it addresses the tool's operational needs reasonably well.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must fully compensate. It provides detailed semantic explanations for all three parameters: 'model_saved_name' and 'model_version' are clearly defined, and 'custom_redteam_model_config' receives extensive documentation with a comprehensive example structure, mandatory/optional fields, and usage instructions. This goes far beyond what the bare schema provides.
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: 'Add a custom use-case based redteam task using a saved model.' It specifies the verb ('Add') and resource ('custom use-case based redteam task'), and distinguishes it from siblings by mentioning 'custom' and incompatibility with audio/image modalities. However, it doesn't explicitly differentiate from 'add_redteam_task' or 'add_agent_redteam_task' among the siblings.
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 usage context: it notes incompatibility with audio/image modalities and includes a prerequisite to ensure the model name is available before calling. However, it lacks explicit guidance on when to use this tool versus alternatives like 'add_redteam_task' or 'add_agent_redteam_task', and doesn't specify exclusions or detailed 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It implies a write operation ('Add a new model') and includes an example config with detailed fields, but doesn't cover permissions, error handling, or side effects. This is adequate but lacks depth 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, but includes a lengthy example that could be streamlined. The example is informative but makes the description verbose; some details (like contact information for early access) are extraneous and reduce efficiency.
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 (1 parameter with nested objects, no annotations, no output schema), the description does a good job by explaining the parameter thoroughly and hinting at the return value. However, it could better address behavioral aspects like authentication or error cases to be fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, but the description compensates fully by providing a detailed example of the 'config' parameter, including nested structure, field meanings, and usage notes (e.g., 'only required if provider type is custom'). This adds significant value beyond the minimal 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 ('Add a new model') and the resource ('using the provided configuration'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'add_model_from_url' or 'modify_model_config', which would be needed 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 Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance to 'Ask the user for all the details before passing the config to the tool,' which is helpful for usage timing. It doesn't mention when not to use this tool or alternatives like 'add_model_from_url,' but the guidance is clear and actionable.
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 the full burden of behavioral disclosure. It effectively describes key behaviors: it's a creation/mutation tool (implied by 'Add'), requires model availability checks, involves user interaction for configuration, and returns a dictionary with response details. It also mentions modality constraints, which adds useful context. However, it doesn't cover aspects like error handling, rate limits, or authentication needs, leaving some gaps.
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 front-loaded with the core purpose, but it includes verbose example code and repetitive instructions (e.g., repeating 'sample_percentage' notes). While the content is valuable, the structure could be more streamlined—some sentences could be condensed without losing clarity, and the example could be referenced more succinctly.
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 (3 parameters, nested objects, no output schema, no annotations), the description does a strong job of covering context. It explains parameters thoroughly, provides usage prerequisites, notes modality constraints, and describes the return value. However, without an output schema, it doesn't detail the structure of the returned dictionary (e.g., specific keys or data types), which is a minor gap in completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must fully compensate. It provides detailed semantics for all three parameters: 'model_saved_name' and 'model_version' are explained, and 'redteam_model_config' receives extensive documentation with an example structure, test types, and usage notes. This adds significant value beyond the bare schema, fully explaining what each parameter means and how to use them.
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: 'Add a redteam task using a saved model.' It specifies the verb ('Add'), resource ('redteam task'), and constraint ('using a saved model'), which is clear and specific. However, it doesn't explicitly differentiate from sibling tools like 'add_agent_redteam_task' or 'add_custom_redteam_task', which would be needed 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 Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool, including prerequisites ('ensure that the model name is available'), alternatives ('save a new model then start the redteaming task'), and context-specific instructions ('Before setting the redteam test config, ask the user which tests they would want to run and the sample percentage'). It also notes modality compatibility ('Tests compatible with audio and image modalities are only: cbrn and harmful'), which helps distinguish appropriate use cases.
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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