> ## Documentation Index
> Fetch the complete documentation index at: https://docs.runcomfy.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction

**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

***

## 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)

***

## Available tools

The MCP server exposes **10 tools** across three categories:

### Deployment management

| Tool                | Description                                                               |
| ------------------- | ------------------------------------------------------------------------- |
| `list_deployments`  | List all deployments in your account                                      |
| `get_deployment`    | Get a deployment's details, including its workflow graph and node schemas |
| `create_deployment` | Create a new deployment from a cloud-saved ComfyUI workflow               |
| `update_deployment` | Update a deployment's hardware, scaling, or enabled status                |
| `delete_deployment` | Permanently delete a deployment                                           |

### Inference

| Tool                 | Description                                                              |
| -------------------- | ------------------------------------------------------------------------ |
| `submit_request`     | Submit an async inference request to a deployment                        |
| `get_request_status` | Poll a request's current status (`in_queue`, `in_progress`, `completed`) |
| `get_request_result` | Fetch the final outputs (hosted URLs) of a completed request             |
| `cancel_request`     | Cancel a queued or running request                                       |

### Advanced

| Tool                  | Description                                                                            |
| --------------------- | -------------------------------------------------------------------------------------- |
| `call_instance_proxy` | Call a ComfyUI backend endpoint on a live instance (e.g., `api/free` to unload models) |

***

## Examples

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

### 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

### 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

### 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)

***

## 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.

### 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.

***

## Next steps

* **[Quickstart](/mcp/quickstart)** — Set up the MCP server in your AI assistant
* **[Tool Reference](/mcp/tool-reference)** — Detailed parameters and examples for all 10 tools
* **[FAQ](/mcp/faq)** — Common questions about the MCP server