mcp -data-exploration

This MCP server provides data exploration capabilities through a dedicated explore-data prompt and a small set of data tools. The core workflow is designed to load a CSV file, explore its structure and trends, and produce actionable insights. Use the explore-data prompt with required inputs such as csv_path (the local path to your CSV) and topic (a short description of what you want to learn, e.g., 'Housing prices in California' or 'Weather patterns in New York City'). The server exposes two built-in tools: load-csv, which reads a CSV into a DataFrame (with csv_path and an optional df_name), and run-script, which executes a Python script. This combination enables you to import data, perform scripted analyses, and iterate on prompts to surface insights without manual data wrangling.

Prerequisites

• Python 3.8+ and pip
• Access to the UV tooling used by this MCP setup (the uv/uvx CLI as shown in the repository)
• A local environment where you can run the MCP server (or a cloud environment with Python installed)

Step 1: Install UV tooling

• Ensure you have the uv/uvx CLI available. If your environment uses uv, install or install/upgrade as appropriate for your setup.

Step 2: Get the MCP server code

• Clone or download the repository containing the Data Exploration MCP server.

Step 3: Install dependencies (if any)

• If there is a requirements.txt or pyproject.toml, install dependencies:

  pip install -r requirements.txt
  # or
  pip install -e .
  

Step 4: Run the MCP server

• Use the configuration shown in the README under mcpServers. For development or testing locally, you can start the server with UV tooling as indicated:

  uvx mcp-server-ds
  

Notes:

• Ensure that the CSV files you load via load-csv are accessible from the server process (correct path permissions).
• If you customize data paths or DF names, you can pass df_name to load-csv for easier reference in subsequent operations.

Tips and caveats:

• The explore-data workflow relies on the load-csv and run-script tools to prepare and analyze datasets. Use run-script to apply Python-based analyses or transformations as part of your exploration prompts.
• When working with large CSVs, consider loading a subset or ensuring sufficient memory in the environment where the MCP server runs.
• The server is designed for interactive exploration via Claude Desktop; ensure Claude Desktop is configured to load the mcp-server-ds configuration as shown in the README.
• If you encounter path or permission issues loading CSVs, verify the csv_path is correct and readable by the MCP server process.
• The current setup does not specify environment variables beyond the MCP config; add any needed API keys or credentials via the env map in mcp_config if you extend the server.