<div align="center">

<img src="https://raw.githubusercontent.com/SuMayaBee/HoloViz-MCP-Server/main/assets/panel.png" height="60" alt="Panel" />     <img src="https://raw.githubusercontent.com/SuMayaBee/HoloViz-MCP-Server/main/assets/holoviews.png" height="60" alt="HoloViews" />     <img src="https://raw.githubusercontent.com/SuMayaBee/HoloViz-MCP-Server/main/assets/hvplot.png" height="60" alt="hvPlot" />

HoloViz MCP Server

Let AI agents create interactive visualizations that render live inside your chat — no code required.

Built with FastMCP · Panel · HoloViews · hvPlot · Bokeh

27 MCP tools · 4 interactive UI templates · live streaming · bidirectional interaction

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Demo

1. Inline Chart

"Create a bar chart comparing programming language popularity: Python=32%, JavaScript=28%, Java=18%, TypeScript=12%, Others=10%"

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2. Panel Widgets & Interactivity

"Build a Panel dashboard with a slider controlling sigma in a normal distribution, updating the histogram in real time"

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3. Streaming / Live Data

"Create a live dashboard showing a real-time sine wave that updates every 500ms"

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4. Remote Data Loading

"Load this dataset and profile it, then show a correlation heatmap: https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv"

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5. Maps

"Plot the top 10 most populous cities in the world on an interactive tile map with population shown as point size"

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6. Multi-Panel Dashboard

"Build a dashboard with 3 panels: a bar chart of fruit sales (Apple=50, Banana=30, Mango=45), a pie chart of the same data, and a summary stats table"

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7. Bidirectional Interaction

"Create an interactive scatter plot of the tips dataset where selecting points in the chart updates a summary statistics table below it"

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Architecture

This project is designed as an MCP-native visualization platform: LLMs call tools, the server validates and executes visualization code safely, and users get live, interactive UIs inline in chat.

Architecture at a glance

<img src="https://raw.githubusercontent.com/SuMayaBee/HoloViz-MCP-Server/main/mcp_architecture.png" alt="Architecture" />

Layer responsibilities

Layer	Responsibility	Key implementation modules
LLM Client Layer	Hosts the chat UX and invokes MCP tools	VS Code Copilot, Claude Desktop, Cursor
MCP Orchestration	Defines tool surface and namespaces	server/main.py, server/compose.py
Validation and Safety	Enforces secure code execution before rendering	validation.py, utils.py, display/database.py
Display Runtime	Runs Panel as managed subprocess, serves rendered apps	display/manager.py, display/app.py, display/endpoints.py
Persistence	Stores every snippet and execution metadata for replay/debug/search	display/database.py
MCP App UI	Renders interactive outputs inline in chat sandboxes	templates/show.html, templates/stream.html
HoloViz Stack	Visualization abstraction and rendering backend	Panel, HoloViews, hvPlot, Bokeh, Param
Data Layer	Ingestion and profiling for local and remote datasets	load_data() tool in server/main.py

End-to-end flow

1. An agent calls a tool such as show or stream.
2. The server runs a 5-layer validation pipeline (syntax, security, packages, extensions, runtime).
3. Validated code/config is sent to the Panel display subprocess via REST.
4. The display server executes and persists the snippet in SQLite.
5. The tool returns either:
   • a Bokeh JSON spec for direct in-chat embedding, or
   • a Panel URL rendered in an iframe.
6. MCP App templates provide rich UX (filters, theme toggle, exports, click-to-insight).

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Development Setup

For contributing or running from source:

1. Install Pixi

curl -fsSL https://pixi.sh/install.sh | bash
source ~/.bashrc

2. Clone and install

git clone https://github.com/SuMayaBee/HoloViz-MCP-Server
cd HoloViz-MCP-Server

pixi install
pixi run postinstall

3. Verify

.pixi/envs/default/bin/hvmcp --version

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Example Prompts

Simple chart:

Create a bar chart showing: Jan=120, Feb=95, Mar=140, Apr=110

Scatter plot:

Show a scatter plot of 50 random points using hvplot

Full dashboard:

Create a dashboard with this sales data:
products=[Apples, Bananas, Oranges, Grapes],
revenue=[500, 300, 450, 200],
units=[50, 30, 45, 20]

Load a dataset:

Load /path/to/data.csv and create a visualization

Live streaming chart:

Create a live streaming chart that updates every second with random values

Explore available tools:

What hvplot chart types are available?
What Panel widgets are available?
Show me the hvplot skill guide

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Features

Core Visualization

• Ask your AI assistant to create a chart — renders inline in the chat via MCP Apps
• Interactive charts (zoom, pan, hover) powered by Bokeh
• Every visualization persisted and accessible via URL
• Works in VS Code Insiders, Claude Desktop, and Cursor

View Code Button

Every chart rendered inline has a View Code button in the toolbar — click it to see the exact Python that generated the visualization, with a one-click copy. Great for learning HoloViz.

Kaggle Integration

Paste any Kaggle dataset or competition URL directly into the chat:

Load https://www.kaggle.com/datasets/uciml/iris and show a scatter plot colored by species

Requires KAGGLE_USERNAME and KAGGLE_KEY in your MCP config env (free Kaggle account).

HuggingFace Datasets

Paste any HuggingFace dataset URL and get instant EDA:

Load https://huggingface.co/datasets/scikit-learn/iris and show a correlation heatmap

HF_TOKEN is optional — only needed for private datasets.

Automatic Chart Recommendations

After load_data(), the server analyses column types and returns up to 3 ready-to-render chart recommendations with working hvplot code — no manual chart selection needed.

Datashader for Big Data

Datasets with >100k rows automatically use datashade=True in all recommended chart code — rendering stays fast regardless of dataset size.

Live Streaming Dashboards

Real-time dashboards with periodic callbacks — sine waves, counters, live feeds — all rendered inline.

Maps

Interactive tile maps using hvPlot + GeoViews:

Plot the top 10 most populous cities on an interactive map with population as point size

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Tools

Tool	Description
show(code)	Execute Python viz code, render as live UI with View Code button
stream(code)	Execute streaming Panel code with periodic callbacks
load_data(source)	Profile a dataset + auto chart recommendations. Supports CSV, Parquet, Kaggle, HuggingFace, S3
validate(code)	Run 5-layer validation before show()
viz.create	High-level: describe a chart in plain config, no Python needed
viz.dashboard	Create a multi-panel dashboard from structured config
viz.stream	Create a live streaming visualization
viz.multi	Create a multi-chart grid with linked selections
pn.list / pn.get / pn.params / pn.search	Panel component introspection
hvplot.list / hvplot.get	hvPlot chart type discovery
hv.list / hv.get	HoloViews element discovery
skill_list / skill_get	Access best-practice guides for Panel, hvPlot, HoloViews
list_packages	List installed packages in the server environment

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Project Structure

src/holoviz_mcp_server/
├── cli.py               # CLI entry point (hvmcp serve / mcp / status)
├── config.py            # Pydantic config + env var loading
├── validation.py        # 5-layer code validation pipeline
├── utils.py             # Code execution, extension detection utilities
│
├── server/              # MCP server layer (FastMCP)
│   ├── main.py          # Main server + core tools (show, stream, load_data, ...)
│   ├── compose.py       # Mounts all sub-servers with namespaces
│   ├── panel_mcp.py     # pn.* tools
│   ├── hvplot_mcp.py    # hvplot.* tools
│   └── holoviews_mcp.py # hv.* tools
│
├── introspection/       # Pure Python discovery functions
│   ├── panel.py         # Panel component discovery
│   ├── holoviews.py     # HoloViews element discovery
│   ├── hvplot.py        # hvPlot chart type discovery
│   └── skills.py        # Skill file loading
│
├── display/             # Panel display server (runs as subprocess)
│   ├── app.py           # Panel server entry point
│   ├── manager.py       # Subprocess lifecycle management
│   ├── client.py        # HTTP client (MCP → Panel)
│   ├── database.py      # SQLite + FTS5 persistence
│   ├── endpoints.py     # REST handlers (/api/snippet, /api/health)
│   └── pages/           # Web UI pages (feed, view, add, admin)
│
├── templates/           # MCP App HTML (inline rendering in chat)
│   ├── show.html        # Chart viewer + click-to-insight
│   └── stream.html      # Live streaming viewer
│
└── skills/              # Best-practice guides (SKILL.md files)
    ├── panel/
    ├── hvplot/
    ├── holoviews/
    ├── param/
    └── data/

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Installation

Prerequisite: Install uv first:

# macOS / Linux
curl -LsSf https://astral.sh/uv/install.sh | sh
# Windows
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.sh | iex"
# Or via pip
pip install uv

<details> <summary><b>VS Code / Copilot Chat (Recommended)</b></summary>

Add to your global ~/.config/Code - Insiders/User/mcp.json or workspace .vscode/mcp.json:

{
  "servers": {
    "holoviz": {
      "type": "stdio",
      "command": "uvx",
      "args": ["--from", "hvmcp", "hvmcp", "mcp"]
    }
  }
}

Open Copilot Chat (Ctrl+Alt+I) → switch to Agent mode → start chatting.

</details>

<details> <summary><b>Claude Desktop</b></summary>

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "holoviz": {
      "command": "uvx",
      "args": ["--from", "hvmcp", "hvmcp", "mcp"]
    }
  }
}

Restart Claude Desktop.

</details>

<details> <summary><b>Cursor</b></summary>

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "holoviz": {
      "command": "uvx",
      "args": ["--from", "hvmcp", "hvmcp", "mcp"]
    }
  }
}

</details>

<details> <summary><b>Claude Code / Other stdio clients</b></summary>

{
  "mcpServers": {
    "holoviz": {
      "command": "uvx",
      "args": ["--from", "hvmcp", "hvmcp", "mcp"]
    }
  }
}

</details>

Optional extras

The base install is lightweight. Add only what you need:

Extra	What it adds	Install
geo	Maps via GeoViews + Cartopy	uvx --from "hvmcp[geo]" hvmcp mcp
bigdata	Datashader for >100k row datasets	uvx --from "hvmcp[bigdata]" hvmcp mcp
kaggle	Kaggle dataset loading	uvx --from "hvmcp[kaggle]" hvmcp mcp
huggingface	HuggingFace dataset loading	uvx --from "hvmcp[huggingface]" hvmcp mcp
all	Everything above	uvx --from "hvmcp[all]" hvmcp mcp

Optional: Kaggle & HuggingFace Integration

To load datasets directly from Kaggle or HuggingFace URLs, add credentials to the env section of your config:

{
  "env": {
    "KAGGLE_USERNAME": "your_kaggle_username",
    "KAGGLE_KEY": "your_kaggle_api_key",
    "HF_TOKEN": "your_huggingface_token"
  }
}

• Kaggle token: kaggle.com → Account → Settings → Create New Token
• HuggingFace token: huggingface.co → Settings → Access Tokens → New token (Read role)

HF_TOKEN is optional — only needed for private HuggingFace datasets. If credentials are not provided, Kaggle/HuggingFace URLs will return a friendly message instead of failing silently.

Example prompts once configured:

Load https://www.kaggle.com/datasets/uciml/iris and show a scatter plot colored by species

Load https://huggingface.co/datasets/scikit-learn/iris and show a correlation heatmap

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License

BSD 3-Clause