opentelemetry-mcp-server is a Python Model Context Protocol server that lets an AI assistant query and analyse distributed traces stored in an OpenTelemetry backend. It supports Jaeger, Grafana Tempo and Traceloop as trace sources, selected through a BACKEND_TYPE environment variable together with a BACKEND_URL and, where the backend requires it, a BACKEND_API_KEY. Beyond generic trace querying it has specialised handling for LLM observability using OpenLLMetry semantic conventions, so questions about model calls are answered in terms the data already carries. Typical uses given in the README are finding expensive API calls, investigating errors, comparing the performance of different models and tracking token usage, all asked from the IDE rather than from a tracing UI. The server is published on PyPI as opentelemetry-mcp and needs no installation step: clients are configured to launch it with pipx run or uvx, which always fetches the current version into an isolated environment. It can also be run from a cloned repository through uv with a directory argument, or through a bundled start_locally.sh wrapper that makes switching backends easier, and it can be registered several times under different names to query more than one backend. The README documents client configuration for Claude Desktop, Claude Code, Cursor, Windsurf and the Gemini CLI, and covers a tools reference, example queries, common workflows and troubleshooting. Python 3.11 or newer is required and the project is Apache 2.0 licensed. It is aimed at engineers running LLM applications who already ship OpenTelemetry traces and want to interrogate them conversationally while debugging.
https://github.com/traceloop/opentelemetry-mcp-server
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