Server Quality Checklist

Tool Scores

• list_modelsA
  3.8/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool lists models and their attributes, implying a read-only operation, but does not explicitly declare side effects, access requirements, or other behavioral traits. It does add useful detail about the content returned (capabilities, default model, config).

  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, concise sentence with the action front-loaded. It contains no filler and earns its place by naming the exact scope and content.

  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 (zero parameters, no nested objects, and an existing output schema), the description is complete. It covers all key aspects: what is listed, the server scope, and the type of information returned.

  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?

  There are zero parameters and the input schema is empty, so the description does not need to explain parameter semantics. The baseline for parameterless tools is 4, and the description provides a clear overview without needing to compensate for schema gaps.

  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 lists models supported by the server along with capabilities, default model, and configuration. The verb 'list' and resource 'models' are specific, and the sibling tools are generation/video tools, making the purpose distinct. However, it does not explicitly mention how it differs from siblings, so it falls slightly short of a 5.

  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?

  No explicit guidance is given on when to use this tool versus alternatives. The context implies it is for checking available models before using generation tools, but there are no clear exclusions or comparisons provided.

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

• get_video_statusA
  4.3/5.0
  Behavior3/5

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

  With no annotations, the description carries the burden. It discloses the download side-effect and default behavior, plus output_dir semantics, but does not explain whether the tool polls/waits for completion, returns immediate status, or how failures are handled.

  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 concise: a one-line summary followed by a focused Args list. Every sentence adds value, with no fluff or repetition.

  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 and the presence of an output schema, the description adequately covers purpose, parameters, download behavior, and a key constraint (24h validity). It misses explicit polling/failure behavior, but is otherwise complete for a status-check tool.

  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?

  Schema coverage is 0%, but the description provides meaningful explanations for all 4 parameters: source and validity of task_id, optional model, download default true, and output_dir relative path semantics. This fully compensates for the bare 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's function: '查询视频生成任务状态' (query video generation task status) with an optional download action. This specific verb-resource pair distinguishes it from sibling tools like generate_video and generate_image.

  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 implies usage context through 'task_id: generate_video 返回的任务 ID' (task ID returned by generate_video), indicating this is a follow-up step after video generation. It also notes a 24-hour validity window, but does not explicitly mention alternatives or when not to use it.

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

• generate_videoA
  4.4/5.0
  Behavior4/5

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

  With no annotations, the description carries full responsibility. It discloses the async nature, long generation time (1-10 minutes), blocking wait behavior, and the ability to continue via get_video_status. It does not mention rate limits or authentication, but core behavioral traits are well covered.

  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 lengthy but well-structured: a brief summary, async behavior note, then a labeled Args list. Each parameter line is concise. No redundant fluff, though the length is justified given the number of parameters.

  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 complex tool with 14 parameters and multiple model families, the description covers the async workflow, parameter semantics, and the relationship with get_video_status. An output schema exists, so return format details are not required. Minor gaps around error handling or prerequisites, but overall complete enough for correct selection and invocation.

  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?

  Schema description coverage is 0%, so the description fully compensates by explaining every parameter's meaning, constraints (e.g., duration 2-15, size '1280x720'), and model-specific differences (wan2.5/2.6 vs 2.7). This is comprehensive and directly actionable, exceeding schema expectations.

  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 '视频生成：文生视频或图生视频', identifying the verb and resource clearly. It distinguishes from siblings like generate_image (image generation) and edit_image, and references get_video_status for querying, making the tool's scope unambiguous.

  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?

  Provides clear workflow guidance: default blocks until completion, and if timeout/interrupted, use get_video_status with the returned task_id. Parameter explanations also include context for choosing between text-to-video and image-to-video models. However, there is no explicit 'when not to use this tool' beyond inferring from alternatives.

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

• generate_imageA
  4.6/5.0
  Behavior5/5

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

  With no annotations provided, the description takes on full responsibility and excels. It discloses model-specific behaviors (e.g., 'z-image 每张单独调用，qwen-image-3.0 单次最多 6 张'), parameter side effects ('quality 在 DashScope 原生接口忽略'), and output details ('url 返回远端 URL + 本地路径；b64_json 额外返回 base64 数据'), providing rich operational context.

  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 front-loaded with a one-sentence summary, followed by a structured bullet list of parameter explanations. Despite its length, each entry provides necessary semantic detail for a 13-parameter tool, with no filler or redundant phrasing.

  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 complexity (13 parameters, no annotations, no schema descriptions), the description is exceptionally complete. It covers all parameter behaviors, model differences, output formats, and local file saving, making it sufficient for an agent to select and invoke the tool correctly without additional context.

  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?

  Schema description coverage is 0%, so the description must compensate, and it does thoroughly. Every parameter is explained with meaningful details: size format conversion ('宽x高' to '宽*高'), model options and defaults, support matrix for negative_prompt and watermark, and seed range [0, 2147483647]. This greatly exceeds the bare 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 opens with '文生图：根据文本提示词生成图片' (text-to-image: generate an image from a text prompt), which clearly states the tool's verb (generate) and resource (image). This distinguishes it from sibling tools like edit_image and generate_video, making the purpose unmistakable.

  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?

  Usage is implied by the '文生图' (text-to-image) label, but there is no explicit guidance on when to choose this tool over alternatives like edit_image or generate_video. The description mentions '参数语义对齐 OpenAI Images API' but does not state exclusions or alternative tool references, so the agent must infer the appropriate context.

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

• edit_imageA
  4.8/5.0
  Behavior5/5

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

  With no annotations, the description carries the full burden and performs admirably. It discloses backend-specific limitations (mask, strength, watermark support differences), supported input formats (local path/URL/data URL), model defaults, and the output_dir saving behavior. This is rich behavioral context beyond simple tool purpose.

  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 organized as a concise bullet list with each parameter on one line, packed with essential information. The front-loading of the purpose sentence and the elimination of redundant wording make it efficient for the tool's complexity.

  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 15 parameters, two backend modes, and an output schema, the description is comprehensive: it covers parameter semantics, backend limitations, defaults, and input formats. The output schema exists, so return-value details are not needed. This description is highly complete for a tool of this complexity.

  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?

  Schema description coverage is 0%, but the description compensates by explaining every parameter (all 15) and adding constraints and defaults not present in the schema, such as mask not being supported by DashScope native API, watermark only for qwen-image-3.0, and strength only for the OpenAI-compatible backend. This fully compensates for the lack of schema 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 it is an image-to-image / editing tool that generates new images from 1-3 reference images and edit instructions. It distinguishes itself from sibling tools like generate_image (likely text-to-image) and video tools by emphasizing the reference-image-based editing workflow.

  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 provides clear context by specifying that it uses 1-3 reference images plus an edit prompt, which implies when to use it (when editing/reference images are available). However, it does not explicitly name alternatives or state when not to use it, such as when starting from text-only, which could be done with generate_image.

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