Retro Diffusion Pixel Art

Generate authentic pixel art - sprites, animations, and tilesets - from any MCP client

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Generate authentic pixel art - sprites, animations, and tilesets - from any MCP client

Список инструментов сервера (20)

Технические названия из tools/list. Нужны только разработчикам.

authenticate	Authenticate this MCP session with a RetroDiffusion public API key.
logout	Clear the authenticated RetroDiffusion API key for this MCP session.
create_user_style	Create a user style from the public RD Pro template using /v2/styles. Use `style_reference_images` and `style_reference_caption` for style-level references. These are baked into the custom style and are not the same as per-inference `reference_images`.
update_user_style	Update a public RD Pro user style using /v2/styles/{style_id}. Use `style_reference_images` and `style_reference_caption` for style-level references. These are baked into the custom style and are not the same as per-inference `reference_images`.
delete_user_style	Delete a user style using /v2/styles/{style_id}. style_id can be either internal id or prompt_style/public_id.
list_available_styles	List publicly available styles from /v2/styles/selector. Use this to discover valid prompt_style values plus style metadata such as require_input_image and supports_reference_images.
list_available_models	Return unique public model identifiers available in /v2/styles/selector.
get_style_usage	Explain how to use a public prompt_style from the RetroDiffusion API. Use this before create_inference if you are unsure whether a style expects `input_image`, supports per-inference `reference_images`, or whether you meant style-level `style_reference_images`.
get_balance	Get credits/balance using the public /v2/inferences/credits endpoint.
estimate_inference_cost	Estimate generation cost using the public /v2/inferences endpoint with check_cost=true. Use `input_image` for the main source image, `reference_images` for extra per-inference guidance, and `style_reference_images` only on create_user_style/update_user_style.
create_inference	Generate images using the public /v2/inferences endpoint. For the highest quality prefer RD Pro styles (rd_pro__); they support reference_images for character/style consistency, and most go as small as 12x12 px (check list_available_styles for each style's limits) — a small target size is never a reason to switch to a cheaper model family. Style ids are opaque strings with no uniform format (some RD Fast styles appear as "default:rd_flux"); take them verbatim from the catalog and never infer capabilities from an id's prefix. For animation styles prefer start_inference_job + get_inference_job instead — animations are long-running, and a failed animation is worth one retry with identical parameters (failures auto-refund). Field-tested workflow rules: N distinct items = N individually usable images (separate calls or num_images=N), never one sheet/grid image unless a sheet IS the deliverable. Variants of ONE image (seasons, day/night, palettes) = generate the base once, then derive each variant with the image_edit tool ("... keep the exact same composition") — independent generations of the "same" scene come out unrelated. Converting an existing image INTO pixel art is rd_pro__pixelate with input_image (16-256 px output, batch<=16); reference_images-based generation re-imagines rather than converts. To animate an image you already have, use rd_advanced_animation__ with input_image (fixed-format rd_animation__* styles generate their own subject from the prompt instead). To get the other directional views of a sprite you already have, use rd_advanced_animation__rotate with input_image (same 8-direction layout as rd_animation__8_dir_rotation). Use `input_image` for the main source image, `reference_images` for extra per-inference guidance, and `style_reference_images` only on create_user_style/update_user_style. The response excludes raw base64 image payloads to keep MCP outputs compact.
list_edit_tools	List the enabled canvas edit tools from /v2/edit/tools with their fields, costs, and limits. Edit tools are the recommended way to post-process generated pixel art: background removal, palette conversion, color reduction, pixel correction, rotation, inpainting, outpainting, seam tiling, and prompt-driven edits.
fix_pixel_art	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.
run_edit_tool	Run a canvas edit tool on an image via /v2/edit/tools/{tool_id}. Free tools: color_reducer, palette_converter, pixel_correction, k_centroid_downscale, rotate. $0.01 tools: background_remover, color_style_transfer. Premium ($0.18): image_edit, inpainting, outpainting, seam_tiling. Use estimate_edit_tool_cost first for anything that charges — estimation is free. The edited image is returned in base64_images.
estimate_edit_tool_cost	Validate an edit-tool request and estimate its cost and duration without running it. Always free.
get_inference_result	Retrieve retained outputs for a synchronous generation. Use the request_id returned by create_inference to recover or refresh signed output URLs. This lookup is read-only; never repeat a paid create_inference call just because delivery URLs are temporarily missing.
start_inference_job	Start a generation as an async job (POST /v2/inferences with async=true) and return a task_id. Recommended for advanced animations (rd_advanced_animation__*), other animation styles, and batches — they run for tens of seconds and can outlive a synchronous MCP call. Poll the returned task_id with get_inference_job roughly every 2-5 seconds.
get_inference_job	Check the status of an async generation started with start_inference_job. Statuses: pending, running, succeeded, failed. On success the result carries hosted output_urls (raw base64 payloads are excluded to keep MCP outputs compact).
list_inference_jobs	List your most recent async jobs, newest first (GET /v2/inferences/tasks). Recovery path: if a submission response was lost (timeout, disconnect) before the task_id arrived, the job was still accepted — find it here instead of re-submitting and being charged twice. Then poll it with get_inference_job.
get_service_status	Check RetroDiffusion subsystem health (/v2/status, no auth). Useful before bulk generation runs.