Allows loading Kaggle datasets by URL for profiling and visualization.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@HoloViz MCP Server Create an interactive scatter plot of tips data"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
DataViz MCP is a local Panel web server and MCP server that executes Python code snippets and renders the resulting visualizations as live, interactive web pages — enabling humans and AI assistants to display and inspect Python outputs in real time.
Two interfaces — pls serve (standalone browser UI) and pls mcp (MCP server for AI assistants)
Any visualization library — hvplot · plotly · altair · matplotlib · seaborn · holoviews · bokeh · and more
Validate before render — show runs syntax, security, package, and extension checks before any rendering happens
Visual validation — screenshot MCP tool lets the AI inspect the rendered output visually before presenting it
Persistent storage — SQLite database with full-text search; every snippet gets its own permanent URL
Auto-restart — Panel subprocess is health-monitored and automatically restarted on failure
Works everywhere — local, JupyterHub, GitHub Codespaces; URLs externalized automatically
Related MCP server: Chart-Output MCP Server
Install via uv, pip, or pixi — see the Installation guide for full instructions including how to find your pls path.
uv tool install "dataviz-mcp[pydata]"Pin your version — this project is in its early stages. Pin to a specific version to avoid unexpected changes:
uv tool install "dataviz-mcp[pydata]==0.1.0a1"
Use the absolute path printed by which pls above — not just pls.
Full setup instructions for each client: docs → Connect to your MCP client
Client | Config location |
VS Code |
|
Cursor |
|
Claude Desktop |
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Claude Code |
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claude.ai | HTTP transport + tunnel — see docs |
$ pls
Usage: pls [OPTIONS] COMMAND [ARGS]...
DataViz MCP - Execute and visualize Python code snippets.
╭─ Options ────────────────────────────────────────────────────────────────────────────────────────────╮
│ --version -V Show version and exit. │
│ --help Show this message and exit. │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Commands ───────────────────────────────────────────────────────────────────────────────────────────╮
│ serve Start the DataViz MCP directly. │
│ mcp Start as an MCP server for AI assistants. │
│ status Check whether the Panel server is running. │
│ list List resources (packages, etc.). │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────╯You can also use dataviz-mcp but pls is shorter and easier to remember.
See the Contributing guide for the full setup (fork, install, connect to MCP client, run tests).
Contributions are welcome! Please follow these steps:
Fork the repository.
Create a new branch: git checkout -b feature/YourFeature.
Make your changes and commit them: git commit -m 'Add some feature'.
Push to the branch: git push origin feature/YourFeature.
Open a pull request.
Please ensure your code passes all tests and linting before submitting.
This server cannot be installed
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Renders interactive Chart.js charts and dashboards inline in AI conversations.
Create, read and live-edit visual boards, Kanban plans, Gantt timelines and diagrams with AI agents.
The all-in-one data stack for agents. Upload files, run SQL, evolve tables, and render charts.
Renders interactive Chart.js charts and dashboards inline in AI conversations. Supports bar, line, area, pie, doughnut, scatter, and radar charts with multi-chart dashboard grids.
