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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:
Instructions for connecting the MCP on different platforms are as follows:

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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.

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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>.

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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_prompt_node, create_code_node, create_input_node, create_image_node, create_note, place_tool, create_artifact_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 & Blender whenever Runchat is open inside the CAD app on the same account — read/build the Grasshopper canvas, run Rhino commands, run Python in Blender, and capture the viewport — grasshopper_api, run_rhino_command, run_blender_command, take_screenshot
  • Run nodes and read the results — run_nodes
  • Publish Create reusable tools and apps from your workflows — publish_runchat
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.

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Using the MCP with Rhino and Grasshopper

The MCP reaches Rhino through your signed-in Runchat plugin session. The plugin just needs to be open — it doesn’t matter which workflow (if any) it’s showing:
  1. Launch Runchat in Rhino and sign in with the same account the MCP is connected to
  2. Ask the agent to run Rhino commands, build a Grasshopper definition, or capture the viewport
The agent still saves scripts and results to a workflow on the canvas. To have it work in a specific one, share that workflow’s URL (or use Copy ID from the Runchat menu); otherwise it can create a new workflow itself.

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Example

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.

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Programmatic access

Prefer a terminal to an MCP client? The @runchat/cli command line is a port of this server — the same tools as scriptable runchat … commands. Or call the Canvas API over plain HTTP.