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RunComfy MCP gives AI assistants direct access to your Serverless API (ComfyUI) deployments. Connect Claude, Cursor, Windsurf, 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

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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 10 tools across three categories:

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Deployment management

ToolDescription
list_deploymentsList all deployments in your account
get_deploymentGet a deployment’s details, including its workflow graph and node schemas
create_deploymentCreate a new deployment from a cloud-saved ComfyUI workflow
update_deploymentUpdate a deployment’s hardware, scaling, or enabled status
delete_deploymentPermanently delete a deployment

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Inference

ToolDescription
submit_requestSubmit an async inference request to a deployment
get_request_statusPoll a request’s current status (in_queue, in_progress, completed)
get_request_resultFetch the final outputs (hosted URLs) of a completed request
cancel_requestCancel a queued or running request

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Advanced

ToolDescription
call_instance_proxyCall a ComfyUI backend endpoint on a live instance (e.g., api/free to unload models)

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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 with your API token in the Authorization: Bearer header.
  2. The MCP server translates tool calls into RunComfy Serverless API requests (api.runcomfy.net) using your token — 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.
The MCP server is stateless — your API token is sent per-request in the Authorization header and is never 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
  • Tool Reference — Detailed parameters and examples for all 10 tools
  • FAQ — Common questions about the MCP server