Server Quality Checklist

Tool Scores

• openai_chat_completionsD
  1.7/5.0
  Behavior1/5

  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 only states the action ('create chat completion') without mentioning any behavioral traits such as authentication requirements, rate limits, cost implications, response format, or error handling. This is inadequate for a tool that likely involves API calls with significant operational considerations.

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

  Conciseness5/5

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

  The description is extremely concise with a single sentence that directly states the tool's purpose. There is no wasted language or unnecessary elaboration, making it front-loaded and efficient in structure.

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

  Completeness1/5

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

  Given the complexity of an OpenAI chat completions API tool with 6 parameters, no annotations, and no output schema, the description is severely incomplete. It fails to explain the tool's behavior, parameter usage, or expected outcomes, leaving critical gaps for an AI agent to understand and invoke the tool correctly.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters1/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  The schema description coverage is 0%, meaning none of the 6 parameters (max_tokens, messages, model, stream, temperature, top_p) are documented in the schema. The description adds no information about what these parameters mean, their expected formats, or how they affect the chat completion. This leaves all parameters semantically undefined.

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

  Purpose2/5

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

  The description 'Create chat completion (POST /v1/chat/completions)' restates the name/title with minimal elaboration. It specifies the verb 'create' and resource 'chat completion', but lacks specificity about what a chat completion entails or how it differs from sibling tools like embeddings creation or model listing. This is a tautological description that provides little additional insight beyond the tool name.

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

  Usage Guidelines1/5

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

  The description provides no guidance on when to use this tool versus alternatives like openai_embeddings_create or openai_models_list. It does not mention any context, prerequisites, or exclusions for usage. This absence of guidance leaves the agent without direction on appropriate tool selection.

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

• openai_embeddings_createC
  2.1/5.0
  Behavior1/5

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

  No annotations are provided, so the description must fully disclose behavioral traits. It only states the action and endpoint, failing to describe critical aspects such as authentication needs, rate limits, response format, or potential side effects (e.g., if it's a read-only or mutating operation). This leaves significant gaps in understanding how the tool behaves.

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

  Conciseness5/5

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

  The description is extremely concise with a single sentence, front-loaded with the key action. There is no wasted text, making it efficient in structure, though this brevity contributes to gaps in other dimensions.

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

  Completeness1/5

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

  Given the complexity of creating embeddings (a mutating operation with parameters), no annotations, no output schema, and 0% schema coverage, the description is incomplete. It lacks essential details about behavior, parameters, and outputs, making it insufficient for an AI agent to use the tool effectively.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters1/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema description coverage is 0%, meaning the input schema provides no descriptions for parameters. The description does not add any meaning beyond the schema, failing to explain what 'input' and 'model' parameters represent, their expected formats, or examples. With 2 parameters and no compensation in the description, this is inadequate.

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

  Purpose3/5

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

  The description states the action ('Create embeddings') and the API endpoint ('POST /v1/embeddings'), which clarifies the verb and resource. However, it lacks specificity about what embeddings are or how they differ from sibling tools like chat completions or model listing, making it somewhat vague in distinguishing its unique purpose.

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

  Usage Guidelines2/5

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

  The description provides no guidance on when to use this tool versus alternatives like openai_chat_completions or openai_models_list. It does not mention any context, prerequisites, or exclusions, leaving the agent without clear usage instructions.

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

• openai_models_listB
  3.3/5.0
  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 the tool lists models via a GET request, implying a read-only operation, but doesn't cover aspects like authentication needs, rate limits, error handling, or the format of returned data. This leaves significant gaps in understanding how the tool behaves in practice.

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

  Conciseness5/5

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

  The description is a single, efficient sentence that front-loads the core action ('List models') and includes essential technical details (the backend and endpoint). There is no wasted verbiage, making it highly concise and well-structured for quick understanding.

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

  Completeness2/5

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

  Given the lack of annotations and output schema, the description is incomplete for a tool that interacts with an external API. It doesn't explain what the return value looks like (e.g., list of model objects), potential errors, or authentication requirements, which are critical for an AI agent to use this tool effectively in real-world scenarios.

  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 documentation is needed. The description appropriately doesn't discuss parameters, focusing instead on the tool's purpose and endpoint. This aligns with the baseline expectation for tools with no parameters.

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

  Purpose4/5

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

  The description clearly states the action ('List models') and the resource ('from OpenAI-compatible backend'), with the specific API endpoint ('GET /v1/models') providing technical context. It distinguishes from siblings like 'openai_chat_completions' and 'openai_embeddings_create' by focusing on model listing rather than chat or embedding operations, though it doesn't explicitly name these alternatives.

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

  Usage Guidelines3/5

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

  The description implies usage for retrieving available models from an OpenAI-compatible API, but it doesn't provide explicit guidance on when to use this tool versus alternatives (e.g., for checking model availability before making chat completions). No exclusions or prerequisites are mentioned, leaving usage context somewhat inferred rather than clearly defined.

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