Skip to main content
Runchat hosts a remote Model Context Protocol server, so any MCP-compatible AI client can work with your canvas directly — listing and creating workflows, building and wiring nodes, editing code, and running executions. The server lives at: 
https://runchat.com/api/mcp

​
Add the connector

The first time you connect, your client walks you through a one-time sign-in (OAuth) and asks you to approve access to your Runchat account. After that it stays connected.
  1. From the Chat or Code tab, click Customize then click Connectors from the sidebar.
  2. Click + from the Connectors toolbar, then Add custom connector.
  3. Name it Runchat and paste https://runchat.com/api/mcp as the URL.
  4. Click Add and then wait for Claude to show the Connect button.
  5. Click Connect to open a browser and launch Runchat. Sign in and approve access when the Runchat consent screen appears.

​
Authenticating with an API key

If your client doesn’t support OAuth, or you’re scripting server-to-server, you can authenticate with a Runchat API key instead of signing in:
  1. Sign into Runchat, click your account button, then Get Runchat API Key.
  2. Create a key and copy it.
  3. Configure your MCP client to send it as a bearer token: Authorization: Bearer <your_api_key>.

​
What the agent can do

Once connected, the agent has the same canvas abilities as the in-app assistant:
  • Find & create runchat workflows — list_runchats, create_runchat
  • Read the canvas and inspect node parameters — get_canvas, read_nodes
  • Build prompt, code, input, image, note, and sub-workflow nodes — create_node, update_node
  • Connect & organize nodes into workflows — connect_nodes, organize_nodes, delete_nodes, delete_edges
  • Edit code in code nodes with find-and-replace or full rewrites — read_files, edit_file, create_files, delete_files, read_status
  • Discover models and their parameters — list_models, get_model_params
  • Find & run published tools — search the tool library (and your own runchats), inspect how a tool is built, and run one directly without adding it to a canvas — search_tools, inspect_tool, execute_tool
  • Load skills for specialized environments (Rhino, Blender, Revit, HTML) — use_skill
  • Drive Rhino & Grasshopper when the Runchat plugin is connected to the runchat workflow — read/build the Grasshopper canvas, run Rhino commands, and capture the viewport — grasshopper_api, run_rhino_command, take_screenshot
  • Run nodes and read the results — run_nodes
The agent can only read, edit, and run workflows you own or that are shared with your team — the same access you have in the app. You can share a URL to a runchat workflow that you want the agent to edit or run, or use Copy ID from the runchat menu and share that instead. The agent can also search your runchats if required.

​
Example

You: Connect to my Runchat workspace and add a code node to the
     runchat workflow "Site Research" that filters the summarizer output
     to only items with a score above 0.8.

Agent: [lists your runchats, opens "Site Research", reads the canvas,
        creates and connects the code node, shares the editor link]
Running nodes consumes credits. The agent will confirm before executing any run_nodes calls. Share the editor link it returns to watch progress or take over in the app.

​
Programmatic access

Prefer plain HTTP without an MCP client? The same canvas operations are available as a REST API — see the Canvas API reference.