Server Quality Checklist

Tool Scores

• get_model_infoC
  2.9/5.0
  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 it 'Get information' but doesn't clarify if this is a read-only operation, what happens if the model doesn't exist (e.g., error handling), or any rate limits or permissions required. This leaves significant gaps in understanding the tool's behavior.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is appropriately sized and front-loaded, starting with the main purpose. The Args and Returns sections are structured clearly, but the formatting with indentation might be slightly verbose. Overall, it's efficient with little waste.

  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) and the presence of an output schema, the description is somewhat complete. However, it lacks behavioral context and usage guidelines, which are important for an AI agent to invoke it correctly. The output schema helps, but the description could do more to explain the tool's role relative to siblings.

  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, which has 0% description coverage. It specifies that the 'model' parameter is the 'Model name to get info for', but this is basic and doesn't provide details like format, examples, or constraints. With one parameter and low schema coverage, the description compensates slightly but not fully.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's purpose with a specific verb ('Get') and resource ('information about a specific model'), making it easy to understand what it does. However, it doesn't explicitly differentiate from sibling tools like 'list_models', which might list multiple models rather than get detailed info about one.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  No guidance is provided on when to use this tool versus alternatives. The description lacks context about prerequisites, such as whether the model must exist or be accessible, and doesn't mention sibling tools like 'list_models' for comparison or 'chat' for different operations.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• list_modelsB
  3.1/5.0
  Behavior2/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  No annotations are provided, so the description carries the full burden of behavioral disclosure. While it states the tool lists models and returns a list of names, it doesn't describe important behavioral aspects like whether this is a read-only operation, if there are rate limits, authentication requirements, or how the list is structured (e.g., pagination, sorting). The description is minimal and lacks behavioral context.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is very concise with two sentences: one stating the purpose and one describing the return value. It's front-loaded with the main action. However, the formatting with indentation and a 'Returns:' section is slightly verbose for such a simple tool, but not wasteful.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness3/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the tool has 0 parameters, 100% schema coverage, and an output schema exists, the description is adequate but minimal. It covers the basic purpose and return type, but lacks context on usage guidelines and behavioral traits. For a simple list tool, this might be sufficient, but it could benefit from more guidance on when to use it 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?

  The tool has 0 parameters, and schema description coverage is 100% (empty schema). The description doesn't need to explain any parameters, which is appropriate. It focuses on the return value instead, which adds value beyond the input 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 with a specific verb ('List') and resource ('all available AI models'). It distinguishes from 'get_model_info' by focusing on listing all models rather than getting detailed information about a specific one. However, it doesn't explicitly differentiate from 'chat' beyond the obvious functional difference.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides no guidance on when to use this tool versus alternatives like 'get_model_info' or 'chat'. It doesn't mention any prerequisites, context, or exclusions for usage. The agent must infer usage from the tool name and description alone.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• chatB
  3.2/5.0
  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. While it mentions the tool chats with AI models, it doesn't describe important behavioral aspects like rate limits, authentication requirements, cost implications, error handling, or whether this is a read-only vs. state-changing operation.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is perfectly structured and concise - a clear purpose statement followed by well-organized parameter explanations and return value description. Every sentence earns its place with no wasted words.

  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 (interactive AI chat) and the presence of an output schema (which handles return values), the description is adequate but incomplete. It covers parameters well but lacks behavioral context that would be crucial for an AI agent to use this tool appropriately.

  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 description adds significant value beyond the 0% schema description coverage by explaining both parameters: 'model' is described as 'Model name from configuration' with examples, and 'inputs' is clarified as 'Chat input (string or OpenAI-format messages)'. 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.

  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 as 'Chat with specified AI model', which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'get_model_info' or 'list_models', which appear to be informational rather than interactive.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides no guidance on when to use this tool versus alternatives. There's no mention of when to choose 'chat' over the sibling tools 'get_model_info' or 'list_models', nor any context about appropriate use cases or limitations.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.