Server Quality Checklist

Tool Scores

• web_searchB
  3.4/5.0
  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 the tool performs a web search without any details on behavior such as rate limits, cost, result format, or synchronous/asynchronous nature. This is insufficient.

  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 very concise, consisting of a clear header and an Args section with a single example. Every word serves a purpose, and it is front-loaded with the main action. No unnecessary 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 the tool is a web search, the description lacks crucial context such as what the search results contain, how many results are returned, whether pagination is supported, or any limitations. Even for a simple tool, more context would be helpful for an AI agent to use it effectively.

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

  Parameters3/5

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

  The input schema has one parameter 'query' with 0% description coverage. The description adds an example query (e.g., 'OpenAI GPT-5 release date'), which provides some context, but does not explain constraints like length limits, allowed formats, or language. This is moderate value.

  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 'Use MiniMax for web search' and provides an example query, making the purpose immediately obvious. The tool name 'web_search' is descriptive, and no sibling tools perform web search, so distinction is clear.

  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 does not provide explicit guidance on when to use this tool versus alternatives. It only implies its use for web search, but lacks any context about when it is appropriate or not, or comparisons to sibling tools.

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

• clear_vision_cacheA
  4.1/5.0
  Behavior3/5

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

  No annotations are provided, so the description must carry the burden. It discloses that it flushes cache to disk and returns statistics, but doesn't clarify if the cache is cleared (destructive) or just persisted, leaving some ambiguity about side effects.

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

  Conciseness5/5

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

  The description is two sentences, focused, and front-loaded. Every word contributes meaning with no fluff.

  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 tool with no parameters and no output schema, the description is fairly complete: it explains the action and what is returned. However, it lacks detail about the statistics returned, such as format or contents.

  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?

  There are no parameters, so the schema fully covers them. The description adds value by stating that the tool returns cache statistics, which is not in the schema.

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

  Purpose5/5

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

  The description clearly states the tool clears the image understanding cache, force writes to disk, and returns statistics. It identifies the specific resource and action, and is distinct from sibling tools like generate_image or understand_image.

  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 says 'manual trigger' implying on-demand use, but does not provide explicit guidance on when to use this tool versus others or when not to use it. Usage is implied but not fully elaborated.

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

• generate_imageA
  4.2/5.0
  Behavior4/5

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

  With no annotations, the description carries the full burden of behavioral disclosure. It details save behavior, response_format, prompt_optimizer, and the dependency of save_to_disk on base64 mode. It omits potential latency and auth requirements, but covers core traits well.

  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 efficiently structured with a head sentence and a list of Args. It front-loads the purpose and avoids fluff, though it repeats default values that are already in the schema. Minor redundancy barely detracts from clarity.

  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 7 parameters, no output schema, and no annotations, the description provides adequate context for prompt, model, aspect_ratio, response_format, n, and optimizer. However, it does not specify the exact output structure or error handling, leaving minor 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?

  Despite 0% schema description coverage, the description compensates fully by explaining all 7 parameters with defaults, constraints (e.g., aspect_ratio options, n range 1-3), and mode interactions (e.g., save_to_disk only for base64). This adds significant value beyond the raw schema.

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

  Purpose5/5

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

  The description clearly states it uses 'MiniMax image-01' to 'generate images' and defaults to local saving. This distinct verb-resource pairing differentiates it from siblings like 'understand_image' and 'web_search'.

  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 explains parameter behavior and defaults but lacks explicit guidance on when to use this tool versus alternatives. It does not mention when not to use it or specify any prerequisites, leaving usage context implicit.

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

• query_quotaA
  4.2/5.0
  Behavior4/5

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

  With no annotations, the description carries full burden. It discloses that the tool uses the configured API key, returns usage info for different model types with cycles, and lists returned data like remaining times and reset times. Minor gap: no mention of error behavior or rate limits.

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

  Conciseness5/5

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

  Three sentences, no wasted words. First line states purpose, second elaborates on specifics, third clarifies parameterless usage. Efficient and well-structured.

  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 no output schema and no parameters, the description is sufficient. It covers key behavioral aspects (cycles, reset times) and configuration. Could list exact return fields, but the '等' (etc.) suffices for the scope.

  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 has 0 parameters, baseline 4. Description adds value by stating no parameters are needed and that the tool uses the API key from config, which is informative beyond the schema.

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

  Purpose5/5

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

  The description clearly states the tool queries the remaining quota of the MiniMax Token Plan. It specifies verb 'query' and resource 'quota', and distinguishes from siblings like image generation or web search.

  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 use for quota inquiries but does not explicitly mention when to use or when not. Siblings are distinct, so no confusion, but explicit guidance on usage context is missing.

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

• understand_imageA
  4.7/5.0
  Behavior5/5

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

  Discloses caching (LRU + disk persistence), mode-specific outputs (quick ~300 words; detailed 7 dimensions), image constraints (formats, size ≤20MB), and references OpenHanako design.

  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?

  Well-structured with sections (modes, cache, args) but some redundancy (mode details in prose and bullet). Slightly verbose but still clear.

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

  Completeness5/5

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

  Comprehensive: covers input, behavior, caching, output (via mode descriptions). No output schema but mode details provide sufficient 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?

  All 4 parameters explained in detail: image_url (URL/local path, formats, limit), prompt (example question), mode (values), use_cache (boolean). Schema has 0% coverage, description fully compensates.

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

  Purpose5/5

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

  Description explicitly states '分析图片内容' (analyze image content) with two modes (quick/detailed). Clearly distinguishes from siblings like generate_image (creation) and web_search (search).

  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?

  Describes when to use quick mode ('适合快速了解图片内容') and detailed mode ('结构化分析'). Does not explicitly exclude alternatives but context clarifies differentiation.

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