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Bridging Kernel Space and AI: Building an MCP Server for Linux Scheduler Observability - Daniel Hodges, Meta#

MCP Dev Summit North America 2026 digest Program Core pick 2–3 April 2026

Abstract

Daniel Hodges, Software Engineer (Linux team), Meta

  • Why: The clearest example at this conference of AI applied to deep systems debugging — an MCP server that exposes kernel-level observability to agents rather than yet another wrapper over high-level APIs.
  • What: A production MCP server implemented in scxtop, the observability tool for Linux's sched_ext extensible scheduler framework, letting developers ask natural-language questions like "why is my application experiencing high scheduling latency?" and get AI-driven analysis correlating kernel tracing data, hardware topology, performance counters, and scheduler internals (from abstract only).
  • How: MCP layer over scxtop's tracing and counter data, so the agent can reason across kernel traces, CPU topology, and scheduler state jointly.
  • Where applicable: Linux environments using sched_ext (the eBPF-based extensible scheduler framework); the tool is open source in the sched-ext/scx repo, so this is usable outside Meta, but it presumes sched_ext adoption.