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RunComfy MCP gives AI assistants direct access to your Serverless API (ComfyUI) deployments. Connect Claude Code, Claude.ai, ChatGPT, Cursor, VS Code, Windsurf, Hermes Agent, OpenClaw, or any MCP-compatible client and manage deployments, run inference, and retrieve results — all from natural language. MCP endpoint: https://mcp.runcomfy.com/mcp Transport: Streamable HTTP Authentication: an Authorization: Bearer API token, or browser sign-in Already have your API token? Grab it from your RunComfy Profile — then the whole setup is one command:
Then try it straight away — ask your assistant:
“List my RunComfy deployments”
If you get back deployment names and IDs, you’re connected. See the Quickstart for every other client.

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What you get

With the RunComfy MCP server, your AI assistant can:
  • List and inspect deployments in your account, including workflow graphs and node schemas
  • Create, update, and delete deployments with full control over hardware and autoscaling
  • Run inference on any deployment using the async queue (submit, poll, fetch results)
  • Cancel queued or running requests to stop unnecessary GPU usage
  • Call ComfyUI backend endpoints on live instances via the instance proxy (e.g., free memory, unload models)

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Available tools

The MCP server exposes 31 tools spanning three RunComfy products, plus your account balance.

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Serverless API (ComfyUI) — deployment management

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Serverless API (ComfyUI) — inference

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Serverless API (ComfyUI) — advanced

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Model API — hosted models, no deployment

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Trainer API — datasets

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Trainer API — training jobs

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Account

These compose: list_models finds a model, get_model shows what it takes, run_model runs it — and a LoRA from get_training_job_result can be passed straight to run_model without deploying anything.

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Examples

Here are typical workflows an AI assistant performs with the RunComfy MCP:

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Generate an image

“Generate an image of a mountain landscape using my Flux deployment”
  1. The assistant calls list_deployments to find your deployments
  2. It calls get_deployment with include_payload=true to inspect the workflow’s node IDs and input names
  3. It calls submit_request with the appropriate overrides (e.g., {"6": {"inputs": {"text": "a mountain landscape at sunset"}}})
  4. It calls get_request_result to fetch the output image URL

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Create and run a new deployment

“Deploy my upscaler workflow and run it on this image”
  1. The assistant calls create_deployment with your workflow_id, workflow_version, and hardware choice
  2. It calls submit_request on the new deployment with image input as a public URL in overrides
  3. It polls get_request_status until the job completes
  4. It calls get_request_result to return the upscaled image URL

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Check and cancel a running job

“What’s the status of my last request? Cancel it if it’s still queued.”
  1. The assistant calls get_request_status with the deployment_id and request_id
  2. If the status is in_queue, it calls cancel_request
  3. The cancel response confirms cancelled or not_cancellable (if already running)

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How it works

  1. Your AI assistant sends MCP tool calls to https://mcp.runcomfy.com/mcp, authenticating either with your API token in the Authorization: Bearer header or with a token from browser sign-in.
  2. The MCP server verifies the credential against RunComfy on every request, then translates the tool call into a RunComfy Serverless API request (api.runcomfy.net) as you — so you see only your deployments and billing is attributed to your account.
  3. Results flow back to the assistant as structured JSON with output URLs, status fields, and metadata.
Because every call is re-checked upstream, regenerating your token in your Profile revokes access immediately. See How is my API token handled? for what is and isn’t stored.

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File inputs

When a workflow requires image, video, or audio inputs, pass them directly in the overrides:
  • Public URL: "image": "https://example.com/photo.jpg"
  • Base64 data URI: "image": "data:image/jpeg;base64,/9j/4AAQ..."
No separate file upload step is needed.

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Next steps

  • Quickstart — Set up the MCP server in your AI assistant, plus troubleshooting
  • Tool Reference — Detailed parameters and examples for all 31 tools
  • FAQ — Common questions about the MCP server