Replicate MCP Server

Retrieve metadata and the OpenAPI input/output schema for a specific Replicate model. Use this before replicate_run_model to know which fields the model accepts and what they mean. Args: - model (string): "owner/name" or "owner/name:version". Returns structuredContent: { "model": string, "description": string | undefined, "visibility": string | undefined, "latest_version_id": string | undefined, "input_schema": object | undefined, // OpenAPI schema for inputs "output_schema": object | undefined, // OpenAPI schema for outputs "example_url": string | undefine…

Retrieve the current status and (if available) outputs of a Replicate prediction by its ID. Use this when a previous generate_* or run_model call returned pending=true (timed out before completion). Args: - prediction_id (string): The ID returned by a previous call. - download (boolean, default true): If the prediction has succeeded, download its outputs locally. Returns: PredictionResult — same shape as replicate_generate_image. If still running, status will be "processing" or "starting" and pending will be true. Typical flow: 1. Call replicate_generate_video → returns pending=true w…

Retrieve the current state of a training run: status, the resulting trained model version (once it succeeds), and any error. Args: - training_id: ID returned by replicate_create_training. Returns structuredContent: TrainingSummary.

Fill masked regions of an image based on a text prompt. Works for both inpainting (replace inside) and outpainting (extend canvas) when the mask covers the target area. DISPLAY REQUIREMENT — embed the result inline using one of the three blocks (iframe / <img> / markdown) printed by the tool. Args: - image (URL): Source image. - mask (URL): Mask image. White = keep, black/transparent = repaint. - prompt: Describes what should appear in the masked region. - model (default "flux-fill-pro"): Curated (flux-fill-pro, sd-inpaint, ideogram-v2-edit) or "owner/name". - extra_input (object, …

Description is built at runtime.

List the deployments on the authenticated Replicate account. A deployment is a private, autoscaled endpoint pinned to a specific model + hardware. Args: - limit (1-100, default 20): How many deployments to return. Returns structuredContent: { count: number, deployments: DeploymentSummary[] }. Each DeploymentSummary has owner, name, and current_release { model, version, hardware, min_instances, max_instances }.

Return the most recent predictions on the authenticated Replicate account. Useful to recover a prediction ID, audit recent calls, or check what's still running. Args: - limit (1-100, default 10): How many predictions to return. Returns structuredContent: { count: number, predictions: PredictionSummary[] } Each PredictionSummary has id, model, status, created_at, completed_at, url.

Return the most recent training runs on the authenticated account. Args: - limit (1-100, default 10): How many trainings to return. Returns structuredContent: { count: number, trainings: TrainingSummary[] }.

Run a directed acyclic graph (DAG) of Replicate predictions as a background job. Returns a pipeline_id immediately. Poll replicate_pipeline_status for per-step progress and results. Independent steps run concurrently. Downstream steps auto-start when their dependencies complete. Use "$stepId.field[n]" template strings to pass one step's output as another step's input. IMPORTANT: model must be a full Replicate identifier ("owner/name" or "owner/name:version"). Curated shortcuts (e.g. "flux-schnell") are not supported — look up the full id via replicate_get_model_schema. Template reference sy…

Poll the status of a pipeline started with replicate_pipeline_start. Args: - pipeline_id (string): Pipeline ID returned by replicate_pipeline_start. - include_outputs (boolean, default true): Include full PredictionResult per step. Set false for a counts-only summary while the pipeline is running. Returns structuredContent: { pipeline_id, overall_status, total, succeeded, failed, skipped, running, pending, created_at, expires_at, steps: [{ id, model, status, prediction_id, result?, error?, skip_reason?, started_at, completed_at }] } overall_status: "running" — steps …

Rank the curated models in a category by a priority (speed, cost, quality, or balanced) and return recommendations with cost estimates and reasoning. This does NOT run anything — it advises which model to use. Workflow: call this to pick a model, then call the matching generate tool (e.g. replicate_generate_image) with model set to the recommended key. Args: - category (required): One of image, video, audio, tts, llm, vision, upscale, bg, stt, inpaint, segment, embed, voiceclone, threed, lipsync. - priority (default "balanced"): "speed" (fastest), "cost" (cheapest), "quality" (best), or …

Search Replicate for popular models NOT yet in the curated registry. Returns suggestions only — does not modify code. Use this to find new models worth adding. Then ask Claude to edit src/models.ts with the ones you want. Args: - categories (string[], optional): Which categories to check. Default: all 15 (image, video, audio, tts, llm, vision, upscale, bg, stt, inpaint, segment, embed, voiceclone, threed, lipsync). - min_run_count (integer, optional): Minimum run_count threshold. Default: 1000. - limit_per_category (integer, optional): Max suggestions per category (1-20). Default: 5. …

Produce a transparent-background version (PNG) of an image. DISPLAY REQUIREMENT — after this tool returns successfully, embed the cut-out image inline using one of the three blocks (iframe / <img> / markdown) printed by the tool. Args: - image (string URL): URL of the source image. - model (string, default "rembg"): Curated key (rembg, birefnet, briaai-rmbg) or "owner/name". - extra_input (object, optional): Model-specific extras. - download (boolean, default true): Download the cut-out PNG locally. Returns: PredictionResult with urls + local_paths to a transparent PNG. Examples: …

Run a prediction against a deployment's current release. WAITS for the prediction to finish and (by default) auto-downloads the outputs locally — same UX as the curated generate_* tools. Args: - deployment: "owner/name" of the deployment to run. - input: model input parameters as a JSON object (same shape the deployment's underlying model expects). - download (default true): download output files locally. - timeout_ms (optional): max ms to wait before returning a pending result you can poll with replicate_get_prediction. Returns the standard prediction result (inline image preview / …

Generic escape hatch: run ANY model in the Replicate catalog by its "owner/name" identifier. This tool gives Claude access to the entire Replicate model catalog — anything not covered by the curated specialised tools (image, video, audio, speech, chat, vision, upscale, remove-bg) can be reached from here. DISPLAY REQUIREMENT — if the result includes image URLs, paste ONE of the embed blocks the tool prints (iframe / <img> / markdown — try in order) verbatim in your reply so the image renders inline in the chat. Use this for any category WITHOUT a curated specialised tool, including but not l…

Search the Replicate catalog by free-text query. Returns up to 25 matching models with names, descriptions, and URLs. Args: - query (string, 1-200 chars): Free-text search. Examples: "image upscaler", "voice cloning", "depth estimation", "code generation". Returns structuredContent: { "count": number, "models": [ { "owner": string, "name": string, "description": string | undefined, "url": string, "run_count": number | undefined, "cover_image_url": string | undefined } ] } Tip: Once you find a promising model, call…

Produce a segmentation mask of an image. Use SAM 2 for point/box-prompt masks (auto-mask everything when no prompt given) or Grounded-SAM for text-prompt masking like "the red car". DISPLAY REQUIREMENT — embed the mask result inline using one of the three blocks printed by the tool. Args: - image (URL): Source image. - prompt (string, optional): Text prompt for grounded segmentation. Required for grounded-sam. - model (default "sam-2"): Curated (sam-2, grounded-sam) or "owner/name". - extra_input (object, optional): SAM-specific tuning (e.g. {points_per_side: 32}).

Transcribe an audio or video file to text using Whisper-family models on Replicate. Args: - audio (URL): URL of the audio (or video) to transcribe. - model (default "incredibly-fast-whisper"): Curated key (whisper, incredibly-fast-whisper, whisperx, scribe) or "owner/name". - language (string, optional): ISO-639 hint (e.g. "en", "it"). Default: auto-detect. - translate_to_english (bool, optional): Translate the transcript to English instead of preserving source language. - extra_input (object, optional): Model-specific extras (e.g. {batch_size: 24} for incredibly-fast-whisper). Ret…

Upload a file to Replicate's file storage and get back a URL valid for ~24 hours. Pass the returned URL as a model input (e.g. image for upscale/inpaint/vision, image_url for video, reference_audio_url for voice clone). Two input modes — provide EXACTLY ONE: - file_path: absolute local path of a file on the machine running the server. - base64_data: the file's bytes as base64 (a bare base64 string OR a full "data:<mime>;base64,..." URI). Use this when you hold bytes in memory but have no local path — e.g. an image a user dropped into the chat that a code container can read and base64-enco…

Upscale an image to higher resolution. Optional face restoration for photos. DISPLAY REQUIREMENT — after this tool returns successfully, embed the upscaled image inline using one of the three blocks (iframe / <img> / markdown) printed by the tool. Place it BEFORE descriptive prose. URLs expire ~24h. Args: - image (string URL): URL of the source image. - model (string, default "real-esrgan"): Curated key (real-esrgan, clarity-upscaler, swinir, gfpgan) or "owner/name". - scale (1-10, optional): Upscale factor. Default 4 for real-esrgan; 2 for gfpgan; 2 for clarity-upscaler. - extra_inp…

Run a vision-language model to describe, caption, or answer questions about an image. Args: - image (string URL): URL of the image to analyse. - prompt (string, optional): Question or instruction (e.g. "describe this image", "count the people"). Default is a generic caption. - model (string, default "llava-13b"): Curated key (llava-13b, llava-v1.6-34b, blip-2, qwen-vl) or "owner/name". - max_tokens (1-4096, optional): Response length. - extra_input (object, optional): Model-specific extras. Returns: PredictionResult with text_output containing the model's textual answer. Examples:…