Server Quality Checklist

Tool Scores

• vision_analyze_videoA
  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 must fully disclose behavior. It mentions server-side processing and local file size limits but fails to describe the return value format, error handling, authentication, or rate limits, leaving significant 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 extremely concise, using two sentences to convey the core purpose, process, and recommendations. Every sentence provides essential information without redundancy.

  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 video analysis, missing annotations, and no output schema, the description should cover return values, error states, and limitations. It only partially addresses input handling and omits output and behavioral details, making it incomplete.

  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 100% schema description coverage, baseline is 3. The description adds value by recommending http(s) URLs and explaining that local files are base64-encoded with size limits, going beyond the schema's parameter descriptions.

  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 performs video understanding by sending video to a cloud multimodal model. It specifies that frame extraction and temporal alignment are handled server-side, distinguishing it from sibling tools like vision_analyze_image which targets images.

  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 recommends using http(s) URLs and notes that local files are base64-encoded with size limits. However, it does not explicitly state when to use this tool versus alternatives (e.g., vision_analyze_image) or when not to use it, providing only partial usage guidance.

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

• vision_analyze_imageA
  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 full burden. It indicates the tool calls a cloud visual large model, implying latency and cost, but does not disclose rate limits, authentication needs, or error handling for invalid inputs. The description adds some context but is not comprehensive.

  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?

  Two sentences efficiently convey the tool's purpose and usage options without waste. The most important information (image analysis, cloud model, flexible input) is 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?

  Given the tool's moderate complexity (5 params, no nested objects, no output schema), the description covers the key aspects: what it does, how to use it (prompt/preset), and input formats. It does not describe the return format, but that is a minor omission for a text-generation 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?

  Schema coverage is 100% with all parameters described. The description adds value by explaining that prompt overrides preset, providing a list of preset options, and clarifying the flexible input formats for the image parameter. This goes beyond the schema's individual descriptions.

  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's function as image understanding using a cloud vision model for tasks like description, OCR, chart/UI/error analysis. The verb 'analyze' and resource 'image' are explicit, and it distinguishes from siblings by focusing on static images rather than video or face detection.

  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 mentions using prompt or preset, but does not explicitly state when to use this tool versus alternatives like vision_analyze_video. It implies usage for static images but lacks explicit when-not or alternative guidance.

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

• vision_get_server_statusA
  4/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries full burden. It indicates a read-only query operation, but lacks details on network calls, caching, or potential impacts. Adequate for a simple check, but not rich.

  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?

  Single sentence with clear, front-loaded information. No wasted words, every part adds value.

  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 covers the basic purpose and what it returns. Could specify output format, but it's fairly complete for a simple query.

  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?

  No parameters exist, so baseline is 4. Description doesn't add parameter info but is not necessary. Schema coverage is 100% (empty) so no deduction.

  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 clearly states it queries server status, including whether the cloud vision model is configured, current model, and configuration items. This differentiates from sibling tools like vision_analyze_image which are for content analysis.

  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?

  Description implies usage for checking service status, but does not explicitly state when to use versus alternatives or provide exclusions. Sibling tool names suggest different purposes, but no direct guidance is given.

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

• vision_image_metadataA
  4.1/5.0
  Behavior4/5

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

  No annotations are provided, so the description carries full burden. It clearly discloses the tool is local, non-cloud, and reads specific metadata types. It lacks details on error handling or behavior with unsupported formats, but overall transparency is good for a read-only tool.

  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, well-formed sentence that front-loads the key action and results. Every word is necessary; there is no redundancy or verbosity.

  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?

  Given the tool's simplicity (one parameter, no output schema), the description fully covers the tool's purpose, what it reads, and its local nature. It provides sufficient context for an AI agent to understand the tool's capabilities and constraints.

  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 100% for the single parameter 'image', which already details the supported input formats. The tool description adds no additional parameter-level meaning beyond what the schema provides, so baseline 3 is appropriate.

  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 explicitly states the tool reads metadata (dimensions, format, color mode, EXIF), specifies local Pillow parsing with no cloud calls, and distinguishes itself from sibling tools like vision_analyze_image (likely more complex) and vision_analyze_video (video). The verb '读取' (read) and resource '元信息' (metadata) are specific.

  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 local metadata extraction is needed ('本地 Pillow 解析，不调用云端'), but does not explicitly state when to use this tool versus alternatives like vision_analyze_image, nor does it provide when-not-to-use guidance. Usage context 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.