Server Quality Checklist

Tool Scores

• edit_imageA
  3.8/5.0
  Behavior3/5

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

  With no annotations, the description carries the full burden. It discloses input formats, conditional parameters, and reference to model capabilities, but it does not mention potential side effects, authentication requirements, rate limits, or response structure beyond the response_format choices. This is moderate transparency but leaves gaps.

  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 well-structured with a one-line purpose followed by a clear 'Args' list. All 7 parameters are covered in a readable format. It is somewhat lengthy and redundant with the schema, but every line provides useful operational guidance, making it efficient for its scope.

  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?

  The tool is complex (7 parameters, capabilities-driven behavior) and lacks an output schema. The description explains parameter usage but does not describe the response object, error scenarios, or end-to-end behavior. It is sufficient for basic invocation but incomplete for fully informed use.

  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 86%, so the schema already documents most parameters. The description largely mirrors the schema text without adding significant new meaning. It does add 'through a tai-openapi edit model' context, but the parameter descriptions themselves are nearly identical, so no extra semantic value is provided.

  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 'Edit an image through a tai-openapi edit model,' using a specific verb and resource. This clearly distinguishes it from sibling tools like generate_image and get_image_model_capabilities, making the tool's purpose 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?

  The description includes conditional usage guidance, such as 'only pass it when capabilities input.size is true' and 'Follow the capabilities input.mask policy.' However, it does not explicitly state when to use this tool over generate_image or list_image_models, leaving the distinction implied rather than explicit.

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

• list_image_modelsA
  3.8/5.0
  Behavior2/5

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

  No annotations are provided, so the description must carry full behavioral disclosure. It states it lists aliases but doesn't mention whether it's a read-only operation, performance characteristics, or error conditions. Minimal behavioral context beyond the action itself.

  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, front-loaded with 'List model aliases', no wasted words. Ideal length.

  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 has no parameters and an output schema exists, the description sufficiently covers the essential information. However, the mention of 'tai-openapi' may be ambiguous to unfamiliar users, and no context is given about the meaning of 'aliases'.

  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 input schema has zero parameters, so the description doesn't need to elaborate on parameter details. The baseline of 4 applies, and the description adds no additional parameter semantics but none are 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 ('List') and identifies the exact resource ('model aliases currently exposed by tai-openapi'), clearly differentiating it from sibling tools like generate_image/edit_image which perform actions.

  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 on when to choose this tool over get_image_model_capabilities or when not to use it. The description implies a use case (listing available aliases) but doesn't state exclusions or alternatives.

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

• get_image_model_capabilitiesA
  3.9/5.0
  Behavior4/5

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

  No annotations are present. The description explicitly states the operation is to 'Describe' and enumerates the exact result fields ('required image count, mask policy, size support, prompt requirement, JSON Schema for parameters, parameters example'), which signals a read-only introspection behavior and sets expectations for what is returned. It does not discuss errors or permission requirements, but for a capability-definition query this is adequate.

  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 compact: one purpose sentence, one sentence on result contents, and an Args block. The Args block is duplicative of the schema, but the overall length is appropriate and the key information 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?

  With only one required parameter and an output schema present, the description need not detail return structures. It covers the operation's intent and the key included fields, making the tool self-contained. It could mention sibling tool alternatives, but that omission affects the usage dimension more than completeness.

  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%; the `model` parameter is already described in the schema as 'Enabled tai-openapi image edit model alias to inspect.' The description's Args section repeats this rather than adding new semantics, so it earns the baseline 3.

  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 'Describe the exact inputs accepted by one enabled image edit model,' which names a clear verb ('Describe'), resource ('image edit model capabilities'), and scope ('one enabled...'). This distinguishes it from sibling tools like list_image_models or edit_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?

  No explicit 'when to use' or alternative references appear. The description implies you should call this when you need a model's input contract, but it does not contrast with list_image_models or edit_image, so guidance is only implicit.

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

• generate_imageA
  4.3/5.0
  Behavior3/5

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

  There are no annotations, so the description carries the burden of behavioral disclosure. It explains parameter defaults and response_format, but doesn't disclose return structure, side effects, permissions, or rate limits. For a generation tool, this is moderate transparency but 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?

  The description is concise and front-loaded with the purpose, followed by a well-organized Args list. Each parameter is clearly documented without unnecessary verbosity. Every line 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?

  With no output schema, the description should clarify what the tool returns. It mentions response_format but doesn't explicitly state the return structure (e.g., object containing URL or base64 data). However, for a generation tool with all parameters documented, it is nearly complete. Missing only a formal note on the return value.

  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?

  The schema has 0% description coverage, so the description fully compensates by explaining every parameter: prompt, size, seed, model, and response_format. It adds meaning beyond the schema with details like seed range, size format, and model default.

  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: 'Generate one image from a text prompt.' It uses a specific verb and resource, and the scope is defined (single image). This distinguishes it from sibling tools like edit_image, which would modify an existing 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 gives clear context: this tool is for generating an image from a text prompt. It doesn't explicitly mention alternatives or exclusions, but the purpose is straightforward. It could mention using edit_image for editing, but the core usage is implied strongly enough to warrant a 4.

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