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Vega-Lite Viewer MCP Server

A Model Context Protocol (MCP) server that enables creating interactive data visualizations using the Vega-Lite grammar. Visualizations are rendered directly inside the chat using MCP Apps — no browser window required.

Usage

Prerequisites

This server requires uv. Install it via:

# Windows
winget install --id=astral-sh.uv -e

# macOS
brew install uv

# Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

macOS note: The curl installer places uv in ~/.local/bin/ and updates your shell profile, but macOS GUI apps like Claude Desktop do not load shell startup files. Install via Homebrew to make uv visible to GUI apps.

See the uv installation guide for more options.

Quick Start

Add the following entry to your Claude Desktop configuration file (accessible via Settings... > Developer > Edit Config):

{
  "mcpServers": {
    "vegalite-viewer": {
      "command": "uv",
      "args": [
        "run",
        "--with-editable",
        "/path/to/mcp-server-vegalite-viewer",
        "mcp-server-vegalite-viewer"
      ]
    }
  }
}

Restart Claude Desktop to apply changes. The server is ready when vegalite-viewer appears in the list of connected MCP servers.

ℹ️ Note: Rendering visualizations inline in the chat requires a client that supports MCP Apps, such as Claude Desktop or Claude.ai.

CLI Reference

Flag

Description

--silent

Show only error messages

--debug

Enable detailed debug logging (also settable via VEGALITE_VIEWER_DEBUG=1)

MCP Tools and Prompt

Tools

Tool

Description

upload_data

Upload a JSON dataset and register it by name for later use in visualizations

visualize_data

Render a registered dataset as a Vega-Lite chart, displayed inline in the chat

Workflow: call upload_data first to register the dataset, then call visualize_data with a Vega-Lite specification to produce the chart. The same dataset can be visualized multiple times with different specs.

Prompt

Prompt

Description

Create a simple chart for a JSON dataset

Instructs the LLM to create a chart of a chosen type (bar, line, pie, etc.) for a provided JSON dataset

Example Prompts

Create a simple bar chart for the following JSON dataset:
[
  {"category": "Alpha",   "value": 4},
  {"category": "Bravo",   "value": 6},
  {"category": "Charlie", "value": 10},
  {"category": "Delta",   "value": 3},
  {"category": "Echo",    "value": 7},
  {"category": "Foxtrot", "value": 9}
]

Using with MCP Inspector

Create an mcp.json file:

{
  "mcpServers": {
    "vegalite-viewer": {
      "command": "uv",
      "args": [
        "run",
        "mcp-server-vegalite-viewer",
        "--debug"
      ]
    }
  }
}

Start the inspector from a terminal:

npx -y @modelcontextprotocol/inspector --config mcp.json --server vegalite-viewer

In your browser:

  • Click Connect to start the server

  • Go to Tools > List Tools to see the available tools

  • Find server logs under Server Notifications and in %TEMP%\mcp_server_vegalite_viewer.log (Windows) or ${TMPDIR:-/tmp}/mcp_server_vegalite_viewer.log (Linux/macOS)

Troubleshooting

Visualization not rendering inline

The client must support MCP Apps. In clients without MCP Apps support the tool still works — the Vega-Lite JSON spec is returned as text, which the LLM can describe or the user can paste into Vega Editor.

Server fails to start in Claude Desktop

Check the Claude Desktop logs:

  • Windows: %LOCALAPPDATA%\Claude\Logs\mcp-server-vegalite-viewer.log

  • macOS: ~/Library/Logs/Claude/mcp-server-vegalite-viewer.log

Or go to Settings > Developer, select vegalite-viewer and click Open Logs Folder.

Still having issues?

Run the server with --debug and open an issue on GitHub with the relevant log output.

Related MCP server: Data Visualization MCP Server

Contributing

See CONTRIBUTING.md for development setup, building the React app, code quality checks, and the release process.

Available Tools

2 tools
upload_dataA

A tool to upload and register a JSON dataset by name for use in subsequent visualizations. When to use this tool:

  • When the user asks to visualize data, call this tool first to register the dataset, then call visualize_data. How to use this tool:

  • Provide a short, descriptive name and the dataset as a list of JSON objects (records).

  • Each object should share a consistent set of keys (fields).

Inputs: data (required), name (required). Returns: result

visualize_dataA

A tool to render a Vega-Lite visualization of a registered dataset directly in the chat. When to use this tool:

  • When the dataset is complex or multi-dimensional and a visual representation would be more informative than text.

  • Not useful for single data points. How to use this tool:

  1. Upload the dataset first using the upload_data tool, then reference it by name here.

  2. Analyze the dataset's structure and fields to determine which fields map to which visual channels (x, y, color, size, etc.).

  3. Construct a Vega-Lite specification for the desired chart type. Consult the Vega-Lite documentation (https://vega.github.io/vega-lite/docs) and example gallery (https://vega.github.io/vega-lite/examples) for guidance.

  4. Ensure the spec is a valid instance of the Vega-Lite schema: https://vega.github.io/schema/vega-lite/v6.json. Do not include a "data" key — it is injected automatically from the registered dataset.

Inputs: name (required), spec (required). Returns: result

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    A4.5/5.0

    Scored across 2 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose: upload_data registers datasets, while visualize_data renders them. The descriptions explicitly define when to use each, leaving no ambiguity.

    Naming Consistency5/5

    Both tools follow the same verb_noun pattern (upload_data, visualize_data), which is consistent and predictable.

    Tool Count4/5

    At 2 tools, the server is slightly under the typical 3-15 range, but the tools form a complete two-step workflow for the narrow purpose of Vega-Lite visualization, so the count is reasonable.

    Completeness4/5

    The upload-then-visualize flow covers the core workflow, but there are no management operations (e.g., listing or deleting datasets), which are minor gaps that agents can work around by re-uploading with a new name.

    Maintenance

    ActivitySlowing
    ResponsivenessNo recent community issues

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