MCP as Observability Interface: Connecting AI Agents to Kernel Tracepoints

7 hours ago

MCP (Model Context Protocol) is emerging as the interface for AI agents to access infrastructure telemetry, with developments from companies like Datadog and security warnings from Qualys.
Datadog released an MCP server linking its dashboards to AI agents, validating the protocol, while Qualys flagged MCP servers as a shadow IT risk due to security issues like static secrets.
There are two approaches: wrapping existing observability platforms (e.g., Datadog) for aggregated data, or building MCP-native observability that provides raw, direct access to data, as demonstrated with an eBPF-based tracer for GPU issues.
An example shows MCP-native observability enabling an AI agent (Claude) to quickly diagnose a GPU latency issue by analyzing raw CUDA traces, something aggregated metrics couldn't reveal.
Security concerns from Qualys are addressed by integrating observability into MCP servers, logging interactions within the same pipeline, enhancing transparency and safety.
The future trend involves expanding MCP-native patterns to areas like network, security, and cost observability, bypassing traditional dashboards to give AI agents direct telemetry access.
The project is open-source, allowing users to try it with tools like Ollama or Claude Code, offering tools for investigation with minimal overhead and no dependencies.

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