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An MCP (Model Context Protocol) server for data transformation and BI charts will allow AI assistants to connect to your data sources, transform data, and generate high-quality visualizations through natural language requests.

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c-cf/BI-Chart-MCP-Server

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BI Chart MCP Server

This project implements the BI Chart MCP Server using Python. Previously, the functionality was prototyped with TypeScript, but the current and maintained version is built with Python.

Project Structure

  • mcp_bi_visualizer/: Contains the main server code and modules.
    • server.py: Main entry point for starting the MCP server.
    • data/: Data loading and processing modules.
      • loader.py
      • processor.py
    • resources/: Modules to manage project resources.
      • manager.py
      • memo.py
    • visualization/: Visualization components.
      • renderer.py
      • vega_lite.py
  • scripts/
    • run_server.py: A script to launch the server.
  • tests/: Unit tests for the server and visualization components.
  • Other files include configuration files (e.g., pyproject.toml, requirements.txt, setup.py) and documentation.

Installation

  1. Clone the repository.
  2. Create a virtual environment and activate it:
    python -m venv .venv
    .venv\Scripts\activate   # On Windows
    
  3. Install the required dependencies:
    pip install -r requirements.txt
    

Running the Server

You can run the server using the provided script:

python scripts/run_server.py

Alternatively, you can start the server directly from the module:

python -m mcp_bi_visualizer.server

Testing

Run the tests using your preferred test runner. For example, with pytest:

pytest

Notes

  • The project has been migrated from a TypeScript-based implementation to Python.
  • For any issues or contributions, please refer to the CONTRIBUTING.md file.

Enjoy using the BI Chart MCP Server!

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An MCP (Model Context Protocol) server for data transformation and BI charts will allow AI assistants to connect to your data sources, transform data, and generate high-quality visualizations through natural language requests.

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