reprise
Server Details
Un-flatten any flat AI design into editable layers, reproduce it bit-perfect, from your agent.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 3.5/5 across 3 of 3 tools scored.
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.
Available Tools
3 toolsautodetectBInspect
Auto-detect the elements (subjects and text) in a design image as relative bounding boxes. Returns: A JSON string with the detected elements (relative bboxes) and their count.
| Name | Required | Description | Default |
|---|---|---|---|
| image | No | The design image as an http(s) URL or a base64 data URL. |
Tool Definition Quality
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. It states the return format (JSON string) but does not disclose whether the tool is read-only, idempotent, or has side effects. This is a significant omission for a detection tool.
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 with two sentences, no wasted words, and directly states purpose and output. It is front-loaded with the action and resource.
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?
The description covers input and output basics but lacks details on coordinate system for relative bboxes, error handling, and assumptions about the input image. Given the tool's simplicity, this is adequate but incomplete.
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% for the single parameter 'image', so baseline is 3. The tool description repeats 'design image' but adds no new semantics beyond what the schema already provides (URL or base64 data URL).
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 detects elements (subjects and text) in a design image and returns relative bounding boxes. It distinguishes from siblings 'diagnose' and 'reproduce' by naming a specific detection task, but does not explicitly contrast usage scenarios.
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 information is provided about when to use this tool versus alternatives like 'diagnose' or 'reproduce'. There is no guidance on prerequisites, context, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
diagnoseAInspect
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. |
Tool Definition Quality
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.
reproduceAInspect
Un-flatten a flat design image into editable layers and reproduce it bit-perfect, returning the fidelity score. Returns: A JSON string with a bit_perfect flag and fidelity metrics (mae, psnr, exact-match %, stray px).
| Name | Required | Description | Default |
|---|---|---|---|
| image | No | The source design image as an http(s) URL or a base64 data URL. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It explains the process and output, including fidelity metrics, but does not disclose potential side effects (e.g., whether the image is saved or if the operation is destructive). It is adequate but not fully transparent.
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 sentence concisely states the action and second sentence describes the return value. No unnecessary words, highly efficient.
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 complex operation (un-flattening and reproducing bit-perfect), the description is fairly complete by specifying the output format. However, it could benefit from mentioning supported image formats or constraints on the input image. No output schema exists, so the description compensates well.
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 parameter 'image' with a clear description. The tool description repeats the return format but adds no additional meaning to the parameter beyond what the schema already provides, so 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-flatting a flat design image into editable layers and reproducing it bit-perfect, specifically returning a fidelity score. This distinctly differentiates it from sibling tools 'autodetect' and 'diagnose'.
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 the tool is for converting flat images into editable layers, but it does not explicitly state when to use it over 'autodetect' or 'diagnose', nor does it provide any prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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