Server Quality Checklist

Tool Scores

• generate_imageC
  2.9/5.0
  Behavior2/5

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

  Annotations already indicate non-readOnly (write operation) and non-idempotent, but description adds no behavioral context beyond what annotations provide. It does not disclose potential side effects, no mention of auth requirements, rate limits, or what happens with different model choices. Descriptions should add value beyond structured fields.

  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?

  Extremely concise—one sentence and a short instruction. No wasted words, but could be restructured to include essential guidance. Concision is high, but at the cost of completeness.

  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?

  Tool has 10 parameters, complex schema with enums, and no output schema. Description fails to explain return values, supported image formats, error handling, or model-specific constraints. Incomplete for a complex generative tool.

  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 coverage is 100%, so baseline is 3. Description provides minor added guidance regarding explicit provider+model but does not add significant meaning beyond what parameter descriptions already cover. Minimal extra value.

  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?

  Clear verb+resource: 'generate image(s) from a text prompt'. Description states action and input. However, it does not explicitly distinguish from sibling tools like 'edit_image' or 'get_model_capabilities', though the tool name itself is unambiguous.

  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?

  Provides implicit usage guidance ('Prefer explicit provider+model; respect model capabilities') but lacks explicit when-to-use, when-not-to-use, or alternatives. No mention of why one would choose this tool over 'edit_image' or when to use 'get_model_capabilities' instead.

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

• edit_imageA
  3.7/5.0
  Behavior4/5

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

  Annotations are present (readOnlyHint=false, openWorldHint=true) and not contradicted. The description adds value by detailing that images can be passed as data URLs/base64/https URLs, that most edit-capable models use only images[0], and that invalid images may be rejected. This goes beyond the bare annotation to clarify input handling behavior.

  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: two sentences immediately stating the core purpose and input format. No filler or redundancy. Every word contributes to the essential message.

  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 has 12 parameters, no output schema, and moderate complexity, the description is too brief. It covers image input format but omits any mention of other parameters (n, size, quality, etc.), return value expectations, model-specific behaviors (like which models support masks), or side effects (directory saving). The agent would have to rely entirely on the schema to understand all options.

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

  Parameters2/5

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

  Schema coverage is 100%, so the schema documents all 12 parameters. The description adds minimal parameter context: it only mentions 'prompt' and 'optional mask' generically. It does not explain 'n', 'size', 'quality', 'negative_prompt', 'directory', etc., missing an opportunity to guide usage of these important parameters.

  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 ('Edit'), the resource ('an image'), and the mechanism ('with a prompt and optional mask'). It also specifies accepted image formats (data URLs/base64/https URLs), which distinctively separates it from sibling tools like 'generate_image' that create new images from scratch.

  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 editing existing images but provides no explicit guidance on when to prefer this over 'generate_image' or 'get_model_capabilities'. It lacks any 'when to use' or 'when not to use' statements, leaving the agent to infer context from sibling names.

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

• get_model_capabilitiesA
  4.1/5.0
  Behavior4/5

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

  Annotations already declare readOnlyHint=true and idempotentHint=true, indicating no side effects. The description adds value by specifying the exact content of the returned metadata (generation/edit/mask/limits) and indicating it is per-model, providing useful behavioral context beyond the annotations.

  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 that front-loads the key information: the action ('Return') and the target ('enabled providers and per-model capability metadata'). Every part of the sentence is informative and earns its place.

  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 simplicity of the tool (one optional parameter, read-only, has output schema), the description fully explains what the tool returns. The presence of an output schema and detailed parameter documentation means the description does not need to cover return values or parameter details further.

  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%, with a detailed description for the optional provider parameter listing the allowed values and their meaning. The tool description adds no additional parameter information, so the baseline score of 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 uses a specific verb 'Return' and clearly identifies the resource: enabled providers and per-model capability metadata including generation, edit, mask, and limits. This distinguishes it from siblings like edit_image and generate_image, which perform edits or generations rather than returning metadata.

  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 checking capabilities before calling sibling tools, but it does not explicitly state when to use this tool over alternatives. For example, it could say 'Use this to discover available providers and model capabilities before calling edit_image or generate_image.' Without such guidance, the description is adequate but not optimal.

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