Server Quality Checklist

Tool Scores

• generate_imageA
  3.6/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries the behavioral disclosure burden. It does disclose two key behaviors: it saves the image locally and returns the absolute path and metadata. It does not mention potential overwrite behavior, default folder location, network/API costs, authentication requirements, or rate limits, leaving some gaps for a tool with no annotation support.

  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 two short, information-dense clauses with no filler. It front-loads the core action, then immediately conveys the outcome and return value, which is exactly what an agent needs to know at a glance.

  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 one required parameter, complete schema coverage, and no output schema, the description covers the essential operational facts: what it does, how it saves output, and what it returns. It is missing only an explicit differentiation from edit_image and finer detail about the returned metadata, but neither prevents correct selection or invocation.

  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%, so the input schema already documents all five parameters, including enums for aspect_ratio and output_format. The description adds little parameter-level meaning beyond reinforcing that the prompt should be detailed and that the model is provided via OpenRouter, so the baseline of 3 is appropriate.

  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 a specific action ('Generates an image from a detailed text prompt'), identifies the model and service, and explains the resulting side effect (saving locally) and return value. It is easily distinguishable from the sibling edit_image by the word 'generates,' though it does not explicitly contrast itself with that sibling.

  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?

  When to use the tool is implied: call it when you need to create a new image from a text prompt. However, there is no explicit mention of the sibling edit_image, no when-not-to-use guidance, and no mention of prerequisites or cases where another tool would be more appropriate.

  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
  Behavior2/5

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

  With no annotations, the description carries the full burden of behavioral disclosure. It does not mention that the tool invokes an external model (OpenRouter), that it produces and saves a new image file, or whether the original file is left untouched. These are relevant side effects for an image-editing tool and are not disclosed.

  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 sentence with no filler; the operation, resource, and input type are front-loaded, and the examples are compactly contained in parentheses. Every part earns its place.

  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 adequately covered for a straightforward call because the schema documents all parameters. However, without annotations or an output schema, the description could usefully disclose that the operation uses a remote model and saves the resulting image to output_path or a default folder.

  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 all six parameters are already documented in the schema. The description adds only illustrative examples of prompt instructions (add elements, change backgrounds, alter style), which adds minor context but does not go beyond the schema's parameter documentation.

  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 specific verbs ('edits or transforms') and a clear resource ('existing image from a local file'), making the tool's function unmistakable. The phrase 'existing image' also implicitly distinguishes it from the sibling generate_image, which creates images rather than modifying an existing one.

  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 clearly implies this tool is for modifying an existing image using natural-language instructions, which is enough to guide selection versus generate_image. It does not, however, explicitly name the sibling or state when to prefer one over the other.

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