mcp_server_public

# Basic MCP Server for Data File Analysis A Model Context Protocol (MCP) server that provides tools for analyzing CSV and Parquet files. ## Features - **CSV File Analysis**: Summarize CSV files by reporting row and column counts - **Parquet File Analysis**: Summarize Parquet files by reporting row and column counts - **Sample Data**: Includes sample user data in both CSV and Parquet formats ## Project Structure ``` mix_server/ │ ├── data/ # Sample CSV and Parquet files │ ├── sample.csv │ └── sample.parquet │ ├── tools/ # MCP tool definitions │ ├── __init__.py │ ├── csv_tools.py │ └── parquet_tools.py │ ├── utils/ # Reusable file reading logic │ ├── __init__.py │ └── file_reader.py │ ├── server.py # MCP server instance ├── main.py # Entry point for the MCP server ├── generate_parquet.py # Script to convert CSV to Parquet └── README.md # This file ``` ## Installation 1. **Install uv** (if not already installed): ```bash curl -LsSf https://astral.sh/uv/install.sh | sh ``` 2. **Create and activate virtual environment**: ```bash uv venv source .venv/bin/activate ``` 3. **Install dependencies**: ```bash uv add "mcp[cli]" pandas pyarrow ``` ## Usage ### Running the Server Start the MCP server: ```bash uv run main.py ``` ### Using with LM Studio To use this MCP server with LM Studio, edit your `mcp.json` file and add: This starts the MCP server for you. ```json { "mcpServers": { "mix_server": { "command": "uv", "args": [ "--directory", "/path/to/your/mcp_server_public", "run", "main.py" ] } } } ``` **Note**: Replace `/path/to/your/mcp_server_public` with the actual path to your mcp_server_public directory. Once loaded you should see all available tools for your local LLM to use. ### Available Tools 1. **summarize_csv_file(filename: str)** - Summarizes a CSV file by reporting its number of rows and columns - Example: `summarize_csv_file("sample.csv")` 2. **summarize_parquet_file(filename: str)** - Summarizes a Parquet file by reporting its number of rows and columns - Example: `summarize_parquet_file("sample.parquet")` ### Sample Data The server includes sample user data with the following structure: - **id**: Unique identifier - **name**: User's full name - **email**: User's email address - **signup_date**: Date when the user signed up ## Development ### Adding New Tools 1. Create a new file in the `tools/` directory 2. Import the MCP server instance: `from server import mcp` 3. Define your tool function with the `@mcp.tool()` decorator 4. Import the new tool module in `main.py` ### Adding New File Formats 1. Add utility functions in `utils/file_reader.py` 2. Create corresponding tools in the `tools/` directory 3. Import the new tools in `main.py` ## Dependencies - **mcp[cli]**: Official MCP SDK and command-line tools - **pandas**: For reading CSV and Parquet files - **pyarrow**: Adds support for reading Parquet files via Pandas ## License This project is open source and available under the MIT License.