reprise

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It mentions the output (JSON string with relative bboxes and count) but does not clarify if the tool is read-only, destructive, or has any side effects. For a detection tool, it is likely non-destructive, but the description omits this clarification.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long, front-loads the core purpose, and contains no filler. Every sentence adds value—first defines the action, second specifies the return format. Ideal conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one parameter, no output schema), the description is nearly complete. It explains input format and output content. Missing only minor behavioral context (e.g., whether it modifies anything). A 4 reflects slight room for improvement.

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

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

Schema coverage is 100% for the single parameter 'image', which is described as an http(s) URL or base64 data URL. The description adds context about returning relative bounding boxes but does not add new meaning to the parameter itself. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool auto-detects elements (subjects and text) in a design image and returns relative bounding boxes. The verb 'auto-detect' and resource 'elements in a design image' are specific and unambiguous. No sibling differentiation needed as 'diagnose' and 'reproduce' are distinct tasks.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives (diagnose, reproduce). The description only explains what the tool does, not the context or prerequisites for use. An agent receives no help deciding if this tool is appropriate for a given request.

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

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

No annotations are provided, so the description carries full burden. It clarifies the return format (JSON string with fidelity metrics) but does not disclose potential side effects, authentication needs, rate limits, or behavior under invalid inputs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: two sentences no wasted words. It front-loads the core action and metrics, then briefly states the return type.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (2 string inputs, no output schema), the description sufficiently covers what it does and what it returns. It explains the metrics and output format, though it could mention error cases or input validation.

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

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

Schema description coverage is 100%, with both parameters described in the schema. The tool description adds overall purpose but no parameter-specific insights beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: scoring how faithfully a reproduction matches an original using specific metrics (MAE, PSNR, exact-match %, stray px). It distinguishes itself from siblings 'autodetect' and 'reproduce' by being about comparison rather than detection or generation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use for comparing two images, but provides no explicit guidance on when to use this tool versus alternatives like 'autodetect' or 'reproduce'. No when-not or context exclusions are mentioned.

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

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

No annotations are provided, so the description carries the burden. It discloses the return value structure (JSON with bit_perfect flag and fidelity metrics), but does not mention potential side effects, required permissions, or failure modes (e.g., if image cannot be unflattened). A score of 3 reflects partial transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long, covering the primary action and return value. It is front-loaded with the key verb 'Un-flatten'. No wasted words, though the second sentence could be integrated more smoothly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simplicity of the tool (1 param, no output schema, no annotations), the description is mostly adequate. However, it lacks context about acceptable image complexity, expected processing time, or how to interpret fidelity metrics. Siblings (autodetect, diagnose) suggest a family of image tools, but no cross-referencing is provided.

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

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

The input schema covers 100% of the single parameter 'image' with a description. The tool description does not add additional semantic meaning beyond what the schema already provides (URL format). Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's action: 'Un-flatten a flat design image into editable layers and reproduce it bit-perfect.' It specifies the output as a fidelity score. However, it does not explicitly differentiate from sibling tools (autodetect, diagnose), which could cause confusion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, limitations, or when not to use it. For an image processing tool, context like acceptable image formats or size constraints would be helpful.

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