CSV Server

A Python MCP (Model Context Protocol) server that enables AI assistants to answer natural language questions about company processes and operational data stored in CSV files. Built with FastMCP and Polars for efficient CSV processing with server-side filter expressions.

Technologies

Technology	Purpose
Python 3.10+	Runtime
FastMCP	MCP server framework
Polars	High-performance CSV loading and filtering
Docker	Containerized deployment
pytest	Testing framework

Available Tools

Tool	Description
list_csv_files	Lists all CSV files in the configured directory
get_csv_schema	Shows column names, data types, and sample rows for a CSV file
ask_csv_question	Queries a CSV file using a Polars filter expression

Quick Start

Without Docker

1. Install dependencies:

   pip install mcp polars
   

2. Place your CSV files in the ./data/ directory (or set CSV_DIR to a custom path).

3. Run the server:

   python server.py
   

   The server starts in stdio mode by default, which is what AI clients like Claude Desktop and Cursor expect.

With Docker

1. Place your CSV files in the ./data/ directory.

2. Build and start the container:

   docker compose up -d
   

   The server runs in HTTP mode on http://localhost:8000/mcp.

3. Verify connectivity:

   curl -X POST http://localhost:8000/mcp \
     -H "Content-Type: application/json" \
     -H "Accept: application/json, text/event-stream" \
     -d '{"jsonrpc":"2.0","method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"0.1"}},"id":1}'
   

Configuration

Environment Variables

Variable	Default	Description
CSV_DIR	./data	Path to the directory containing CSV files
MCP_TRANSPORT	stdio	Transport mode: stdio or http
FASTMCP_HOST	127.0.0.1	Host address for HTTP mode
FASTMCP_PORT	8000	Port for HTTP mode

AI Client Integration

OpenCode

Add to your opencode.json:

Local (stdio):

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "csv-mcp": {
      "type": "local",
      "command": ["python", "server.py"],
      "environment": {
        "CSV_DIR": "./data"
      },
      "enabled": true
    }
  }
}

Docker (HTTP):

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "csv-mcp": {
      "type": "remote",
      "url": "http://localhost:8000/mcp",
      "enabled": true
    }
  }
}

Claude Desktop / Cursor

For stdio mode:

{
  "mcpServers": {
    "csv-mcp": {
      "command": "python",
      "args": ["/path/to/server.py"],
      "env": {
        "CSV_DIR": "/path/to/data"
      }
    }
  }
}

For Docker (HTTP):

{
  "mcpServers": {
    "csv-mcp": {
      "url": "http://localhost:8000/mcp"
    }
  }
}

Usage Examples

Once connected, ask your AI assistant questions like:

• "List all available CSV files"
• "Show the schema of processes.csv"
• "Find all rows where status equals 'active' in processes.csv"

The ask_csv_question tool accepts Polars filter expressions:

Expression	Description
pl.col("status") == "active"	Filter by exact match
pl.col("prazo") > 10	Filter by numeric comparison
pl.col("processo").str.contains("aprov")	Filter by substring match

Testing

# Run all tests
python -m pytest tests/ -v

# Run with coverage
COVERAGE_FILE=/tmp/.coverage python -m pytest tests/ --cov=server --cov-report=term-missing

Security

• Path traversal protection: All file paths are resolved and validated to stay within CSV_DIR
• Expression sanitization: Filter expressions are checked against dangerous patterns (import, exec, eval, etc.) before evaluation