Observe Instrument Mcp
Instrument Python AI agents with OpenTelemetry tracing so LangGraph, CrewAI, and similar stacks become debuggable in production.
Overview
observe-instrument-mcp is a MCP server for the Operate phase that instruments Python AI agents with OpenTelemetry tracing across LangGraph, LlamaIndex, CrewAI, and OpenAI SDK.
What is this MCP server?
- Adds OpenTelemetry instrumentation aimed at Python AI agent runtimes
- Documents support for LangGraph, LlamaIndex, CrewAI, and OpenAI SDK—4 major stacks
- PyPI package observe-instrument-mcp v0.1.2 with stdio MCP transport
- LiteLLM-backed model routing via LLM_MODEL plus provider API keys
- Environment-driven setup for Anthropic, OpenAI, Gemini, and Groq keys
- Package version 0.1.2 on PyPI
- 4 documented Python AI frameworks
- 5 named LLM-related environment variables in server schema
What problem does it solve?
Agent pipelines fail opaquely in production because LangGraph and CrewAI steps lack unified traces across tools and model calls.
Who is it for?
Solo builders shipping Python agent products who already plan to send OTel to Jaeger, Grafana Tempo, or a hosted APM.
Skip if: Pure frontend apps, teams that refuse OpenTelemetry, or Node-only agents with no Python runtime.
What do I get? / Deliverables
After install and env configuration, your agents emit OpenTelemetry traces your MCP-aware workflow can drive for faster incident diagnosis.
- Stdio MCP entry point wired for agent-side instrumentation commands
- Framework-aware tracing hooks for LangGraph, LlamaIndex, CrewAI, and OpenAI SDK paths
Recommended MCP Servers
Journey fit
Tracing and telemetry belong in Operate because they sustain reliability after you ship agent features, not while you first scaffold UI. Monitoring is the canonical shelf for OpenTelemetry spans, latency visibility, and cross-framework trace correlation.
How it compares
Production tracing MCP for Python agents, not a generic logging skill or cloud deploy template.
Common Questions / FAQ
Who is observe-instrument-mcp for?
Indie developers running Python-based AI agents in Claude Code or other MCP clients who need OpenTelemetry visibility across framework-specific runtimes.
When should I use observe-instrument-mcp?
Use it in Operate when agents are live or staging and you need spans to debug tool loops, model latency, and multi-agent handoffs.
How do I add observe-instrument-mcp to my agent?
Install observe-instrument-mcp from PyPI, configure stdio MCP in your client, set ANTHROPIC_API_KEY or other provider keys and LLM_MODEL, then connect your OTel exporter per upstream docs.