Server Quality Checklist

Tool Scores

• glm_chat_completionsA
  3.6/5.0
  Behavior2/5

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

  No annotations are provided, so the description carries full responsibility for behavioral disclosure. It only states that it sends messages and returns a JSON response, without mentioning side effects (e.g., token consumption/cost), authentication requirements, streaming behavior, or error conditions. This is insufficient for a complex API call.

  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 structured clearly with an intro, usage bullets, and returns section. It is compact, but the model list duplicates the schema enum and could be trimmed. Still, no sentence is wasted overall.

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

  Completeness3/5

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

  Considering the tool's complexity (29 parameters), full schema coverage, and an output schema, the description provides adequate high-level guidance. It does not discuss nuances like streaming, tool calling, or error handling, but these are optional given the schema's richness. It is complete enough for selection but not for deep invocation 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?

  The input schema has 100% parameter coverage with detailed descriptions, so the baseline is 3. The tool description adds no parameter-level details beyond repeating the model list from the schema, thus not exceeding the schema's contribution.

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

  Purpose5/5

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

  The description clearly states the verb and resource: 'Create a GLM chat completion'. It explains what it does (sends messages, returns generated response) and is easily distinguished from sibling tools (glm_list_models, glm_get_usage_guide) by focusing on the core chat generation functionality.

  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 explicitly lists when to use the tool via 'Use this when' scenarios: chatting with a Zhipu GLM model, Chinese language tasks, and reasoning capabilities. It does not mention when not to use it or explicitly name alternatives, but the context 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.

• glm_list_modelsA
  3.6/5.0
  Behavior3/5

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

  With no annotations, the description must carry behavioral disclosure. It indicates the tool returns a formatted list, which implies read-only behavior, but it does not explicitly mention safety, side effects, or any limitations. This is adequate for a simple listing tool but lacks depth.

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

  Conciseness3/5

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

  The description is short but contains redundancy, repeating the return information in the prose and the 'Returns:' line. It is not as sparse as ideal, but it is still generally concise and front-loaded.

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

  Completeness4/5

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

  For the tool's complexity, the description is fairly complete. It states what it does and what it returns, and since an output schema exists, it does not need to detail return values further. It lacks usage guidelines, but that is covered separately.

  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 zero parameters, and the schema coverage is 100% (empty schema). The baseline for 0 params is 4, and the description does not need to explain parameters. It adds no parameter details because none exist.

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

  Purpose5/5

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

  The description clearly states 'List all available GLM models for the GLM API' with a specific verb-resource combination. It also adds that models come with descriptions, distinguishing it from sibling tools like glm_chat_completions and glm_get_usage_guide.

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

  Usage Guidelines2/5

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

  The description provides no guidance on when to use this tool versus alternatives. It does not mention any specific use cases, exclusions, or sibling tools, leaving the agent to infer usage solely from the name and basic purpose.

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

• glm_get_usage_guideA
  4/5.0
  Behavior3/5

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

  With no annotations provided, the description carries the burden of behavioral disclosure. It states 'Returns: Complete usage guide for GLM tools,' which indicates the output, but it does not mention side effects, authentication requirements, or whether the guide is static or dynamically generated. This is minimally adequate for a read-only documentation 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 somewhat repetitive: the first sentence says 'Get a comprehensive guide,' the second says 'Provides detailed information,' and the third repeats 'Complete usage guide.' It is front-loaded and short, but the redundancy means not every sentence earns its place.

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

  Completeness4/5

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

  For a zero-parameter tool with an output schema, the description is sufficiently complete. It states the purpose and the return value. It could add guidance on when to call it relative to other GLM tools, but this is not essential given the tool's simplicity.

  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 zero parameters, so the baseline is 4. The description mentions 'parameters' as part of the guide's content, but this is not a parameter-semantics issue for the tool itself. No further explanation is needed.

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

  Purpose5/5

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

  The description uses a specific verb+resource: 'Get a comprehensive guide for using the GLM tools,' and further details the content ('parameters, examples, and best practices'). This clearly distinguishes it from siblings like glm_list_models and glm_chat_completions.

  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 makes the tool's purpose evident: it is for obtaining a guide to using GLM tools effectively. However, it does not explicitly state when not to use it or mention alternatives, though the sibling tools are clearly different in function.

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