mcp-plotting-server

A FastMCP server that turns JSON data into Plotly figures, deployable as an isolated service on Modal. Data goes in as JSON; a validated Plotly figure (JSON) or a rendered PNG comes back over the Model Context Protocol.

Why a separate plotting server

Plotting code runs inside this server process, deployed as its own service. The application server that calls these tools never executes plotting code and only ever receives the finished figure or image. The MCP server is the isolation boundary, which is the whole point of the architecture.

Related MCP server: mcp-plots

Tools

All figure-returning tools give back the output of fig.to_dict(), which a frontend renders directly with plotly.js. That JSON is the stable cross-language contract.

Prerequisites

• Python 3.11+

• A Modal account and the CLI logged in (pip install modal && modal token new)

• gh CLI for creating the GitHub repo (optional)

Local development

Run over stdio (the default MCP transport, for use with a local client):

uvx --from . mcp-plotting-server

Or run the server directly:

python -m mcp_plotting.server

For an HTTP server during local development, call mcp.run(transport="http", port=8000) and connect to http://localhost:8000/mcp.

Deploy on Modal

Dev (live-reloading, temporary URL):

modal serve deploy.py

Production (persistent URL):

modal deploy deploy.py

Modal prints a web URL like:

https://<workspace>--mcp-plotting-server-serve.modal.run

The MCP endpoint is that URL plus /mcp. A health check lives at /health.

Connect from an MCP client

Point any MCP client (opencode, Claude Desktop, etc.) at the deployed endpoint:

{
  "mcpServers": {
    "plotting": {
      "url": "https://<workspace>--mcp-plotting-server-serve.modal.run/mcp"
    }
  }
}

Example tool call

quick_plot with a few records:

{
  "data": [
    {"month": "Jan", "sales": 120},
    {"month": "Feb", "sales": 150},
    {"month": "Mar", "sales": 180}
  ],
  "kind": "bar",
  "x": "month",
  "y": "sales",
  "title": "Quarterly sales"
}

Returns a normalized Plotly figure object. Hand the data and layout straight to plotly.js, or pass the spec to render_figure_png for a static image.

Layout

mcp_plotting/server.py   FastMCP server and tools
deploy.py                Modal deployment (ASGI over Streamable HTTP)

License

MIT