Retro Diffusion Pixel Art MCP

Fix Pixel Art

fix_pixel_art

Read-onlyIdempotent

Recover the native pixel grid from enlarged, softened, AI-rendered, or compressed pixel art.

Standard uses the native Rust detector. Neural uses the neural reconstruction engine and accepts optional target width and height values. This repairs existing art—it does not generate a new image. Both engines are free and share a limit of 10 requests per minute per API token. Provide exactly one source as base64 input_image or a public HTTPS image_url. URL input avoids the ALB request-body limit. Decoded images may contain up to 16 megapixels. Successful response JSON is limited to 850,000 bytes for AWS ALB compatibility.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`width`	No	Optional neural target dimension in pixels. Accepted only with engine='neural'.
`engine`	No	Fixer engine. Standard uses the native Rust detector; neural uses the neural reconstruction engine.	standard
`height`	No	Optional neural target dimension in pixels. Accepted only with engine='neural'.
`image_url`	No	Public HTTPS PNG or JPEG URL to reconstruct. Provide this or input_image, not both. URL downloads may contain up to 20 MB and bypass the API request-body limit.
`rd_api_key`	No	RetroDiffusion API key (rdpk-...) for this call only; overrides session or header auth.
`input_image`	No	Base64 PNG or JPEG to reconstruct at its native pixel grid. Raw base64 or a data URL; provide this or image_url, not both. Decoded input may contain up to 16 megapixels, and complete base64 request JSON must stay within 900,000 bytes.
`timeout_seconds`	No	Read-timeout override in seconds for this call; increase for animations or large batches.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.1/5.0

Behavior4/5

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

Annotations indicate read-only, non-destructive behavior, which the description confirms by stating it 'repairs existing art—it does not generate a new image.' The description adds details beyond annotations: rate limits (10 req/min), image size limits (16 MP), response size (850KB), and URL input benefits. No contradictions. Could mention synchronous/asynchronous behavior, but overall strong.

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

Conciseness4/5

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

The description is a single paragraph of 7 sentences, starting with the core purpose. It is informative without extraneous details. Could be slightly more structured (e.g., bullet points for limits) but is efficient and front-loaded.

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 7 parameters, complete schema coverage, annotations, and an output schema, the description covers purpose, engines, input options, and important limits. It does not explain the output format, but that is reasonable given the output schema exists. Minor gaps: no mention of error handling, but overall comprehensive.

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 description's added value over the schema is minimal. It repeats that width/height are for neural engine and that input_image and image_url are mutually exclusive. This is helpful but does not provide new meaning beyond what is already in the structured fields. Baseline 3 is appropriate.

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 clearly identifies the tool's action—recovering the native pixel grid from various degraded pixel art—and distinguishes the two engines (standard and neural). It is specific and differs from sibling tools, which cover authentication, inference, style management, etc. No ambiguity.

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 explains when to use each engine and how to provide input (URL vs base64), including the rate limit. While it doesn't explicitly list alternatives or exclusions, the sibling tools are very different, making usage context clear. Missing explicit 'when not to use' keeps it from a 5.

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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TDQS

A4/5.0

Disambiguation4/5

Most tools have clearly distinct purposes (e.g., create_inference vs start_inference_job vs get_inference_result). However, the difference between create_user_style/update_user_style and per-inference references could still cause confusion, and list_available_models/list_available_styles overlap slightly.

Naming Consistency5/5

All tool names consistently follow a verb_noun pattern (e.g., create_inference, get_balance, list_edit_tools, delete_user_style). No mixing of camelCase or other styles, making the surface highly predictable.

Tool Count5/5

With 20 tools covering authentication, inference (sync/async), styles, editing, cost estimation, and status, the count is appropriate for a pixel art generation API. Each tool addresses a distinct need without bloat.

Completeness4/5

The tool set covers the full lifecycle: auth, cost estimation, synchronous/async generation, style management, editing, and result retrieval. A minor gap is the lack of a tool to list or manage user styles (e.g., get_user_styles), but this is non-critical for core workflows.

Resources

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