MDF
Provides a Model Context Protocol server for AI assistants to access and analyze ASAM MDF automotive and industrial measurement data.
About
This tool serves as a Model Context Protocol (MCP) server, specifically engineered to enable AI assistants to interact with and analyze ASAM MDF (Measurement Data Format) files. It bridges the gap between large language models (LLMs) and complex automotive and industrial measurement data, allowing AI systems to seamlessly access, process, and derive insights from sensor readings, vehicle bus data, and more. The server supports all major MDF versions (2.x, 3.x, 4.x), offers robust data analysis functionalities, and provides versatile export options, making it a powerful component for AI-driven data exploration in the automotive domain.
Key Features
- Efficient session management to handle multiple MDF files and sessions concurrently
- Comprehensive support for ASAM MDF file versions 2.x, 3.x, and 4.x
- Versatile data export capabilities to formats such as CSV, HDF5, Parquet, and MATLAB
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- Advanced data analysis tools including statistics, plotting, and signal processing
- AI-native interface designed for seamless integration with LLMs and AI assistants
Use Cases
- Enabling AI assistants to open, manage, and retrieve metadata from ASAM MDF measurement files.
- Allowing LLMs to query and analyze specific data channels (e.g., engine speed, temperature) within automotive datasets.
- Facilitating AI-driven generation of statistics and plots from time-series measurement data for diagnostic or performance analysis.
