From TCP Retransmits to MCP-Driven Cluster Investigations: An eBPF GPU Agent Retrospective
Seven weeks, ten releases. How an eBPF GPU agent grew an MCP tool surface that drives NCCL stall investigations from TCP retransmits to cluster scale.
mcp
MCP Shows What the Agent Did. eBPF Shows Why the GPU Stalled.
MCP exposes the agent’s actions: which tools, which arguments, which return values. eBPF exposes the kernel-level cause behind the latency the agent surfaced. We walk through a transcript that uses both.
MCP Tools Are New API Surfaces. eBPF Sees What They Actually Touch.
MCP servers are shipping at a pace where the agent-side caller no longer knows which kernel resources its tool calls are actually touching. An eBPF view of the same call returns the answer in one trace.
Agent + MCP + eBPF: 10,869 CUDA Kernel Events, Now Queryable
We gave Claude access to 10,869 CUDA Runtime API events via MCP. It found the root cause of a 124x DataLoader slowdown in 47 seconds by running SQL against a live eBPF trace database.
What Happens When an AI Agent Gets Kernel-Level GPU Traces
Claude Code with MCP access to kernel-level GPU traces, pointed at a real performance anomaly. The full investigation session: what the agent called, what it found, and where it got things right and wrong.
MCP as Observability Interface: Connecting AI Agents to Kernel Tracepoints
MCP is becoming the interface between AI agents and infrastructure data. Three implementations examined side by side: Datadog, Qualys, and a kernel-level tracer that lets agents query eBPF tracepoints directly.
Catching a vLLM Latency Spike with eBPF and an Open-Weight LLM
MiniMax M2.7 running locally via Ollama, connected to a real GPU trace database through MCP. No Claude, no cloud API keys. The model found why vLLM blocked all requests for 11 seconds.