🚀 CSV MCP Server — Bring Your Data to Life in Claude

Why This Matters

If you've ever tried to analyze CSV files in Claude.ai, you know the pain — it can't read your files directly. You end up:

• Copying and pasting CSV snippets (and hitting character limits)

• Uploading your data to other tools

• Manually describing what's inside your CSV

That's time-consuming and breaks your workflow.

Enter the CSV MCP Server

This lightweight connector lets Claude directly access and analyze your local CSV files — privately, efficiently, and in real time.

With it, you can simply say:

"Claude, show me all customers from New York with purchases over $1000,"

and Claude will query your actual file.

What This Server Does

Once connected, Claude can:

✅ List your local CSV files

✅ Preview structure and sample data

✅ Run queries using natural language or Pandas syntax

✅ Generate quick summaries (mean, median, count, etc.)

✅ Handle large datasets — all without sending data online

Everything runs locally — your files never leave your system.

Why It's Different

• Local-first & private: your data stays on your device

• Fast: built using FastMCP

• Extendable: easily add Excel, TSV, or custom logic

• Seamless: Claude becomes your personal data assistant

Quick Start

Requirements

• Python 3.11 or higher

• Claude Desktop app

• uv (recommended package manager)

1. Clone the Repository

git clone https://github.com/Navneet1710/csv_mcp_server.git
cd csv_mcp_server
uv init .

2. Install Dependencies

Using uv, add the required packages:

uv add fastmcp
uv add pandas

3. Set Your CSV Directory

Open main.py and edit line 14 to match where your CSV files are stored:

CSV_DIRECTORY = Path.home() / "Documents" / "csv_files"  # customize this if needed

4. Run the Server

Start the MCP server with:

uv run main.py

For development or inspection mode (to see tools, logs, and capabilities):

uv run fastmcp dev main.py

Connecting to Claude Desktop

Open your Claude configuration file:

Add this MCP server entry:

"csv-analyzer": {
      "command": uv,
      "args": [
        "--directory",
        "path\\to\\csv_mcp_server",
        "run",
        "main.py"
      ]
}

Save and restart Claude Desktop.

Using It Inside Claude

Once connected, try commands like:

List all CSVs

"What CSV files are available?"

Preview a file

"Show me the first few rows of sales_data.csv."

Run queries

"From customer_data.csv, show all entries where purchase > 500."

Summarize data

"Give me the average revenue in financial_report.csv."

Analyze relationships

"Find the correlation between 'age' and 'satisfaction_score' in survey_results.csv."

Tools and Resources

Default Folder Structure

By default, CSV files live in:

Documents/
└── csv_files/
    ├── sales_data.csv
    ├── customer_info.csv
    ├── inventory.csv
    └── financial_report.csv

Customization

• Change CSV directory → edit CSV_DIRECTORY in main.py

• Add support for Excel/TSV → update the file-reading logic

• Create your own tools → add new @mcp.tool() functions for custom analysis

Troubleshooting

"Directory does not exist"

→ Make sure the path exists or create it:

mkdir -p ~/Documents/csv_files

"File not found"

→ Check spelling and ensure the file is inside your configured directory.

Claude doesn't detect the server

→ Restart Claude Desktop and confirm the config file path.

Permission issues

→ Ensure Claude has read access to the directory (run as admin on Windows if needed).

Debugging

Run directly:

uv run main.py

Contributing

Got an idea or improvement? Contributions are welcome!

1. Fork this repo

2. Create a feature branch

3. Add your changes

4. Submit a pull request

License

Open source — use, modify, and share freely.

Built With

• FastMCP — for fast, local MCP integration

• pandas — for data manipulation and statistics

• Anthropic's MCP — for connecting Claude to your environment

Turn Claude Into Your Personal Data Analyst

Set this up once — and from then on, you can explore and analyze your CSVs right inside Claude.

No uploads, no manual parsing, no limits.

Run it locally. Keep your data private. Get instant insights.

Make sure you don't forget to star the repo