Score how faithfully a reproduction matches an original (MAE / PSNR / exact-match % / stray px). Returns: A JSON string with the fidelity metrics.
| Name | Required | Description | Default |
|---|---|---|---|
| original | No | The original image as an http(s) URL or a base64 data URL. | |
| reproduction | No | The reproduced image to compare, as an http(s) URL or a base64 data URL. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It mentions the return type (JSON string) but does not disclose any side effects, auth requirements, or other behavioral traits. Adequate but not rich.
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?
Two sentences: first states purpose and metrics, second states return format. Front-loaded, no unnecessary words.
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?
Covers main purpose and return type, but lacks detail on JSON key structure or metric units. With no output schema, more specificity would be helpful for an AI agent. Adequate for simple tool.
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% with descriptions for both parameters (original and reproduction). The description adds no additional meaning beyond the schema, so baseline score of 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?
Description clearly states the tool scores reproduction fidelity with specific metrics (MAE, PSNR, etc.) and returns a JSON string. Distinguishes from siblings 'autodetect' and 'reproduce' by focusing on evaluation.
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 explicit guidance on when to use this tool versus siblings. The description only states what it does, leaving the agent to infer usage context from the tool name and sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The three tools have distinct primary purposes: detection, scoring, and reproduction. However, reproduce also returns fidelity metrics which overlap with diagnose, potentially confusing an agent on which to use for scoring.
All tool names are single lowercase verbs (autodetect, diagnose, reproduce), following a consistent and predictable pattern.
With only 3 tools, the server is under-scoped for general design image processing, lacking basic operations like loading or editing images. However, for a focused pipeline, it might be acceptable.
The tool surface lacks fundamental operations such as image input/output or layer manipulation, creating significant gaps that would cause agent failures in most workflows.