mcp-server-mdf 0.1.0

MCP Server for ASAM MDF file analysis

MDF MCP Server

A Model Context Protocol (MCP) server for analyzing ASAM MDF (Measurement Data Format) files. Enables AI assistants to access and analyze automotive and industrial measurement data.

🚀 Quick Start

Using uvx (Recommended)

# Install uv (if not already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Run the server directly
uvx mcp-server-mdf

Local Development

# Clone and setup
git clone https://github.com/shanko26/mdfmcp
cd mdfmcp

# Install in virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -e .

# Run the server
mcp-server-mdf

📋 Features

• MDF File Support: Versions 2.x, 3.x, and 4.x
• AI-Native Interface: Designed for LLM integration
• Data Analysis: Statistics, plotting, signal processing
• Format Export: CSV, HDF5, Parquet, MATLAB
• Session Management: Multi-file support
• High Performance: Efficient large file handling

🔧 Configuration

For Claude Desktop

Edit your configuration file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %AppData%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "mdf": {
      "command": "uvx",
      "args": ["mcp-server-mdf"]
    }
  }
}

For VS Code with Continue.dev

Add to your MCP configuration:

{
  "mcpServers": {
    "mdf": {
      "command": "uvx",
      "args": ["mcp-server-mdf"]
    }
  }
}

For Cursor IDE

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "mdf": {
      "command": "uvx",
      "args": ["mcp-server-mdf"]
    }
  }
}

📊 Usage Examples

Open and Analyze MDF Files

User: Open the measurement file at /data/test_drive.mf4
Assistant: I'll open that MDF file for you.

[Opens file]
Successfully opened test_drive.mf4:
- Session ID: mdf_a3b2c1d4
- Version: 4.10
- Channels: 847
- Duration: 1823.4 seconds

List and Search Channels

User: What temperature channels are available?
Assistant: Let me search for temperature-related channels.

Found 23 temperature channels:
- Engine_Temp (°C) - Engine coolant temperature
- Oil_Temp (°C) - Engine oil temperature
- Ambient_Temp (°C) - Outside air temperature

Analyze and Plot Signals

User: Show me the engine speed statistics and plot it
Assistant: I'll analyze the engine speed signal.

Engine Speed Analysis:
- Mean: 2,847 RPM
- Max: 6,234 RPM
- Std Dev: 1,123 RPM

[Shows time-series plot of engine speed]

🛠️ Available Tools

Session Management

• open_mdf - Open an MDF file
• close_mdf - Close a session
• list_sessions - Show active sessions
• get_file_info - Get file metadata

Data Access

• list_channels - List available channels
• mdf_get - Extract single channel data
• mdf_select - Extract multiple channels
• mdf_get_master - Get time channel data

Analysis

• calculate_statistics - Compute signal statistics
• plot_signals - Create visualizations
• mdf_to_dataframe - Convert to pandas DataFrame

Data Manipulation

• mdf_cut - Extract time slice
• mdf_filter - Filter specific channels
• mdf_resample - Change sampling rate

Export

• mdf_export - Export to various formats
• mdf_convert - Convert between MDF versions
• mdf_save - Save modified file

🐳 Docker Deployment (Alternative)

Build Image

docker build -t mcp-server-mdf .

Run Container

# Basic run
docker run -it --rm mcp-server-mdf

# With volume mount for data
docker run -it --rm -v /path/to/mdf/files:/data mcp-server-mdf

# With custom environment
docker run -it --rm -e MAX_SESSIONS=20 mcp-server-mdf

MCP Configuration for Docker

{
  "mcpServers": {
    "mdf": {
      "command": "docker",
      "args": ["run", "--rm", "-i", "-v", "/path/to/data:/data", "mcp-server-mdf"]
    }
  }
}

🧪 Testing

# Run tests
pytest tests/

# Test server manually
python tests/manual_test.py

📁 Project Structure

mdfmcp/
├── src/mdfmcp/
│   ├── __init__.py
│   └── server.py          # Main MCP server
├── tests/
│   ├── conftest.py
│   ├── manual_test.py
│   └── test_server.py
├── examples/
│   ├── basic_usage.py
│   └── test_data_generator.py
├── Dockerfile
├── requirements.txt
├── pyproject.toml
└── README.md

🔧 Development

Setup Development Environment

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Install in development mode
pip install -e .

Code Quality

# Format code
black src/

# Lint
ruff src/

# Type checking
mypy src/

🚨 Troubleshooting

Common Issues

1. Memory errors with large files

   • Use memory="low" when opening files
   • Reduce concurrent sessions

2. Cannot find channels

   • Channel names are case-sensitive
   • Use regex patterns for flexible searching

3. Docker build fails

   • Ensure Docker is running
   • Check Dockerfile syntax

🙏 Acknowledgments

• Built on asammdf by Daniel Hrisca (LGPL v3+)
• Uses the Model Context Protocol by Anthropic
• Matplotlib for plotting capabilities
• Pandas and NumPy for data processing

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

Dependencies: This project uses asammdf which is licensed under LGPL v3+. The asammdf library remains a separate component and is not modified by this project.