runcomfy-com/runcomfy-mcp

MCP server for the RunComfy platform — Serverless API (ComfyUI), Model API, and Trainer API. Manage deployments, run hosted models, train LoRAs, and retrieve results from AI assistants like Claude, Cursor, and Windsurf.

Endpoint: https://mcp.runcomfy.com/mcp

Docs: docs.runcomfy.com/mcp

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What it does

31 tools mirroring the RunComfy docs 1:1, across three products plus your account balance.

Serverless API (ComfyUI) — your own workflows on dedicated endpoints

Docs: docs.runcomfy.com/serverless

Category	Tools
Deployment management	list_deployments, get_deployment, create_deployment, update_deployment, delete_deployment
Inference	submit_request, get_request_status, get_request_result, cancel_request
Advanced	call_instance_proxy

Model API — hosted catalog models, on demand

Docs: docs.runcomfy.com/model-apis

Category	Tools
Catalog	list_models, get_model, list_model_categories
Inference	run_model, get_model_request_status, get_model_request_result, cancel_model_request

No deployment to manage and per-request billing. list_models browses the catalog by keyword or capability (category=image-to-video), get_model returns one model's input schema — property types, defaults, enums, and ranges — and run_model runs it. So an assistant can go from "make me a video" to a valid request without leaving the tools or guessing a parameter.

Entries also carry description, base_price_usd per price_unit, and a model_url to the model's page.

model_id is the identifier shown on the model's page at runcomfy.com/models, slashes included — e.g. blackforestlabs/flux-1-kontext/pro/edit. File inputs must be public HTTPS URLs.

Trainer API — datasets and AI Toolkit LoRA training

Docs: docs.runcomfy.com/trainer-apis

Category	Tools
Datasets	create_dataset, list_datasets, get_dataset_status, delete_dataset
Dataset uploads	upload_dataset_file_from_url, upload_dataset_text_file, get_dataset_upload_urls
Training jobs	submit_training_job, get_training_job_status, get_training_job_result, cancel_training_job, resume_training_job, edit_training_job

Typical flow: create a dataset → upload media and matching .txt captions → poll until READY → submit a job with an AI Toolkit YAML config → poll status → pull checkpoints from the result.

Because the server runs remotely it cannot read local files. Upload media it can reach over HTTP with upload_dataset_file_from_url, write captions inline with upload_dataset_text_file, and for local or >150 MB files use get_dataset_upload_urls and PUT the bytes to the signed URL yourself.

Account

Category	Tools
Balance	get_balance

One wallet funds all three products. get_balance reports what is left, in balance_usd for reading and balance_microdollars (millionths of a dollar) for exact threshold checks. It is served from api.runcomfy.net rather than mirrored per product, because there is only one figure to report.

Crossing between them

A trained LoRA runs without any deployment: pass its base model's model_id to run_model and the LoRA as an input, e.g. {"lora": {"path": "my_first_lora_3000.safetensors"}} — either a name from your LoRA Assets or a public URL such as a checkpoint from get_training_job_result. For a dedicated endpoint with chosen hardware, deploy it and use the Serverless tools instead.

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Quick setup

Every client authenticates with a RunComfy API token from your Profile page. Two ways to supply it:

• API token header — works in any Streamable HTTP client. Simplest, and the only option for clients without a browser OAuth flow.
• Browser OAuth — no token in a config file. Supported by Claude.ai and by local clients that register a loopback callback, such as Claude Code.

Claude Code

Token header (one command, nothing else to do):

claude mcp add --transport http runcomfy https://mcp.runcomfy.com/mcp --header "Authorization: Bearer YOUR_RUNCOMFY_TOKEN"

Or browser OAuth — omit the header, then run /mcp inside Claude Code and pick Authenticate:

claude mcp add --transport http runcomfy https://mcp.runcomfy.com/mcp

--transport http is the Streamable HTTP transport. streamable-http is not a value Claude Code accepts, and single-dash -transport / -header are not either — both forms fail before the server is ever contacted.

Check it with claude mcp list, which should show runcomfy: connected.

Claude.ai

Add https://mcp.runcomfy.com/mcp in Settings → Connectors → Add custom connector, then select Connect. Claude discovers RunComfy's OAuth 2.1 endpoints, opens a RunComfy consent page, and asks for one of the API tokens shown in your RunComfy Profile. The token is validated by RunComfy and encrypted inside the MCP authorization grant; it is never returned to Claude.

Cursor

.cursor/mcp.json:

{
  "mcpServers": {
    "runcomfy": {
      "url": "https://mcp.runcomfy.com/mcp",
      "headers": { "Authorization": "Bearer YOUR_RUNCOMFY_TOKEN" }
    }
  }
}

VS Code (Copilot)

.vscode/mcp.json:

{
  "servers": {
    "runcomfy": {
      "type": "http",
      "url": "https://mcp.runcomfy.com/mcp",
      "headers": { "Authorization": "Bearer YOUR_RUNCOMFY_TOKEN" }
    }
  }
}

Windsurf

Settings → MCP:

{
  "mcpServers": {
    "runcomfy": {
      "serverUrl": "https://mcp.runcomfy.com/mcp",
      "headers": { "Authorization": "Bearer YOUR_RUNCOMFY_TOKEN" }
    }
  }
}

Any other client

• URL: https://mcp.runcomfy.com/mcp
• Transport: Streamable HTTP
• Auth: Authorization: Bearer <token> on every request, or OAuth 2.1 with a loopback redirect URI

Troubleshooting

Symptom	Cause
401 with RunComfy rejected this API token	The token is wrong, expired, or truncated on copy. Generate a new one in Profile — the response body names the fix.
401 with no error_description	No Authorization header reached the server. Check the header is quoted as one argument: --header "Authorization: Bearer ...".
invalid_client_metadata during OAuth	The client registered a non-loopback, non-hosted redirect URI. Use the token header instead.
invalid_target during OAuth	The configured URL must be exactly https://mcp.runcomfy.com/mcp — no trailing slash. RFC 8707 binds the token to that exact resource.
503 with Retry-After	RunComfy's API could not be reached to verify the token. Retry.
429	More than 600 token-authenticated requests a minute from one IP.

Revoke access by regenerating the token in your RunComfy Profile. That invalidates the token header and any OAuth grant built on it, because every MCP request revalidates the token upstream.

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Architecture

MCP Client ──RunComfy API token──┐
                                 │   Cloudflare Worker (/mcp)
MCP Client ──MCP OAuth token─────┤   validates the credential, resolves
                                 │   it to one user's RunComfy token
                                 ▼
                         Cloudflare Container
                         (Python FastMCP app)
                                 │ request-scoped RunComfy credential
                 ┌───────────────┼───────────────┐
                 ▼               ▼               ▼
        api.runcomfy.net  model-api.       trainer-api.
         (Serverless)     runcomfy.net     runcomfy.net
                            (Model)          (Trainer)

One RunComfy token authenticates all three products, so the same credential resolution covers every tool.

Both credential kinds converge on the same request-scoped identity header before the container is reached. They are told apart by shape: OAuth access tokens are always userId:grantId:secret, and a RunComfy API token never contains a colon.

• Cloudflare Worker (src/index.ts) — OAuth 2.1 authorization server and protected-resource boundary. Missing, invalid, expired, or wrong-audience credentials are rejected before MCP initialization or tool discovery.
• Direct API token (src/index.ts) — a RunComfy Profile token presented as Authorization: Bearer is revalidated against api.runcomfy.net on every request, rate-limited per source IP, and never forwarded as-is.
• OAuth consent (src/oauth-bridge.ts) — validates an existing RunComfy Profile token, stores it only in encrypted OAuth grant data, and issues a separate audience-bound MCP access token. Dynamic client registration accepts loopback callbacks (Claude Code and other local clients) plus an exact allowlist of hosted client callbacks.
• Python container (server.py) — FastMCP app with 31 tools across the Serverless, Model, and Trainer APIs. It has no shared/operator credential and fails closed unless the authenticated edge supplies the current user's request-scoped RunComfy token.
• Cloudflare Container auto-starts on first request, sleeps after 10 minutes idle.

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Project layout

.github/workflows/deploy.yml  CI: typecheck, test, deploy to Cloudflare
src/index.ts          Cloudflare Worker entrypoint
src/oauth-bridge.ts   OAuth consent and RunComfy token validation
server.py             MCP tool definitions (31 tools)
runcomfy_client.py    RunComfy API clients (serverless, model, trainer)
container_app.py      ASGI middleware (request IDs, token forwarding)
container_entrypoint.py  Uvicorn startup
container_runtime.py  Env validation, structured logging
wrangler.jsonc        Cloudflare Worker + Container config
Dockerfile            Container image
.env.example          Local dev config

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Local development

# Python 3.11+
python3.11 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env
python -m container_entrypoint

Local endpoints:

• http://127.0.0.1:8000/healthz
• http://127.0.0.1:8000/mcp

The local Python endpoint intentionally has no shared fallback credential. Protected tool calls must go through the authenticated Worker boundary.

---

Deploy

Pushing to main deploys automatically via .github/workflows/deploy.yml: typecheck, Worker tests, and container tests must pass, then wrangler deploy --containers-rollout immediate ships the Worker and the Python container together. Pull requests run the same checks without deploying. The workflow can also be run by hand from the Actions tab.

One repository secret is required:

Secret	Purpose
CLOUDFLARE_API_TOKEN	A token with Edit Cloudflare Workers permission on the account in wrangler.jsonc
CLOUDFLARE_ACCOUNT_ID	Optional. account_id is already committed in wrangler.jsonc; set this only to deploy under a different account.

To deploy by hand (requires Cloudflare Workers Paid plan with Containers enabled):

npm install
npm run check
npm test
npx wrangler deploy --containers-rollout immediate

The MCP endpoint goes live at https://mcp.runcomfy.com/mcp (custom domain configured in wrangler.jsonc).

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Environment variables and bindings

There is deliberately no shared RunComfy API-key secret. OAuth state is kept in the OAUTH_KV binding and every upstream request is tied to the user who authorized the OAuth grant.

Worker vars (in wrangler.jsonc)

Name	Default	Description
CONTAINER_INSTANCE_NAME	runcomfy-unified	Durable Object instance name
CONTAINER_STARTUP_TIMEOUT_MS	15000	Max wait for container start
CONTAINER_PORT_READY_TIMEOUT_MS	30000	Max wait for port ready
MCP_MAX_BODY_BYTES	1048576	Max request body size
OPENAI_APPS_CHALLENGE	Current submission token	Public OpenAI domain-verification token, served verbatim at GET /.well-known/openai-apps-challenge; unset or empty returns 404. Verify ownership of any existing token before replacing it.
MCP_DIRECT_TOKEN_RATE_LIMITER	600 / 60s	Per-IP cap on API-token-authenticated /mcp requests
RUNCOMFY_SERVERLESS_BASE_URL	https://api.runcomfy.net	Serverless API base URL
RUNCOMFY_MODEL_API_BASE_URL	https://model-api.runcomfy.net	Model API base URL
RUNCOMFY_TRAINER_API_BASE_URL	https://trainer-api.runcomfy.net	Trainer API base URL

Local Python dev (.env file)

Name	Required	Description
RUNCOMFY_SERVERLESS_BASE_URL	No	Override Serverless base URL (default: https://api.runcomfy.net)
RUNCOMFY_MODEL_API_BASE_URL	No	Override Model API base URL (default: https://model-api.runcomfy.net)
RUNCOMFY_TRAINER_API_BASE_URL	No	Override Trainer API base URL (default: https://trainer-api.runcomfy.net)
RUNCOMFY_MCP_MOUNT_PREFIX	No	Path prefix for MCP mount (default: empty)