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Retro Diffusion Pixel Art

by ai.retrodiffusion

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
NameRequiredDescriptionDefault
widthNoOptional neural target dimension in pixels. Accepted only with engine='neural'.
engineNoFixer engine. Standard uses the native Rust detector; neural uses the neural reconstruction engine.standard
heightNoOptional neural target dimension in pixels. Accepted only with engine='neural'.
image_urlNoPublic 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_keyNoRetroDiffusion API key (rdpk-...) for this call only; overrides session or header auth.
input_imageNoBase64 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_secondsNoRead-timeout override in seconds for this call; increase for animations or large batches.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, non-destructive. The description adds behavioral details like image size limits (16 MP decoded), response size limit (850 KB), and rate limit (10 req/min), which are not in annotations. No contradiction with annotations.

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 front-loaded with purpose and organized into engine options, input instructions, and limitations. Each sentence adds value, though some detail (e.g., exact byte limits) could be delegated to schema. Still, it's efficient and well-structured for an AI agent.

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?

Given 7 parameters, annotations, and presence of output schema, the description covers purpose, input modes, engine behavior, rate limits, and response constraints. It does not explicitly guide engine selection criteria or explain response structure, but the output schema can cover the latter. Overall complete for effective use.

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

Parameters4/5

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

With 100% schema description coverage, baseline is 3. The description enriches parameter meaning: it explains engine choice implications (e.g., width/height only with neural), input constraints (image size, JSON size, URL limit), and timeout override usage. This goes beyond repeating schema descriptions.

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 states the tool's function: recovering native pixel grid from various degraded forms. It distinguishes itself from sibling tools (e.g., generation tools) by explicitly stating it repairs rather than generates, and mentions two distinct engines.

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

Usage Guidelines5/5

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

The description provides comprehensive guidance: how to provide input (base64 or URL), engine choice (standard vs neural), rate limit (10 req/min), and the fact that it repairs rather than generates. It also notes that URL input avoids the request-body limit, helping the agent decide which input method to use.

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

A4.1/5.0
Disambiguation5/5

Each tool targets a distinct operation: authentication, inference (sync/async/edit), style management, listing, and utility. Even similar tools like create_inference and start_inference_job are clearly differentiated by synchronous vs async execution.

Naming Consistency5/5

All tool names follow the same verb_noun snake_case pattern (e.g., create_inference, get_balance, list_available_styles). No mixed conventions or inconsistent verb forms.

Tool Count4/5

With 19 tools, the count is slightly above the typical 3-15 range, but each tool serves a specific and necessary function for pixel art generation, editing, and management, so it remains well-scoped.

Completeness5/5

The tool set covers the full workflow: authentication, cost estimation, synchronous and async generation, style CRUD, edit tools, pixel fixing, and system status. No obvious gaps for the intended domain.

Resources