Server Quality Checklist

Tool Scores

• edit_imageC
  2.9/5.0
  Behavior2/5

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

  With no annotations provided, the description carries the full burden of disclosing side effects and behaviors. It merely says 'Edit' without mentioning that it modifies images, whether originals are preserved, any API key requirements, or the fact that output_path will not overwrite existing files (as noted in the schema). This lack of transparency is a significant gap for a mutation 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, front-loaded sentence with zero wasted words. It efficiently conveys the core purpose 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 tool's complexity (9 parameters, mutation operation) and the absence of an output schema, the description is far too brief. It doesn't explain expected return values, prerequisites like api_key or base_url, or how the tool integrates with the sibling generate_image. The description is minimally sufficient only for basic understanding.

  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 78%, which is close to the high threshold, so the schema already documents most parameters. The description itself adds no parameter-level detail, falling to the baseline score of 3.

  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 action (edit) and the resource (one or more input images) with the specific model (gpt-image-2). It implicitly distinguishes from the sibling generate_image by focusing on editing existing images rather than creating new ones, though it doesn't explicitly name the alternative.

  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?

  The description gives no guidance on when to use this tool versus generate_image or any other alternatives. There are no stated prerequisites, exclusions, or contextual cues beyond the core action.

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

• generate_imageB
  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 alone must disclose behavioral traits, but it only mentions generation with reference-image guidance. It omits important behavior such as file-saving side effects (output_path), overwrite policy, API-key requirements, or what happens on failure.

  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, no fluff, front-loads the core purpose and key differentiator (optional reference images). Every word contributes.

  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?

  With no output schema and no annotations, a 9-parameter generation tool needs more than a two-sentence description to be fully actionable. Missing return-value behavior, usage boundaries relative to edit_image, and side-effecting save behavior for output_path.

  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 high (78%), so the description need not restate parameters; it does add one useful semantic link by saying reference images are optional guidance. Otherwise, it adds little beyond the schema's own 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?

  Clearly states a specific action ('Generate') and resource ('one image'), identifies the model (gpt-image-2), and notes optional reference images; this distinguishes it from the sibling edit_image tool.

  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 the tool is for generating new images, not editing, but never explicitly tells the agent when to choose this over edit_image or any exclusion criteria. No alternative tool is named.

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