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The Model Context Protocol enables AI assistants to interface with external tools and data sources, but most examples focus on high-level APIs and databases. This talk explores building a production MCP server that exposes low-level Linux kernel observability data to AI assistants, enabling natural language debugging of complex systems.
`scxtop` is an observability tool for Linux's new sched_ext extensible scheduler framework (https://github.com/sched-ext/scx/tree/main/tools/scxtop). By implementing MCP, it allows developers to ask questions like "Why is my application experiencing high scheduling latency?" and receive AI-driven analysis that correlates kernel tracing data, hardware topology, performance counters, and scheduler internals.
Daniel Hodges is a software engineer on the Linux team at Meta. He has previous worked in areas such a observability, profiling, and application performance testing.
