MCP server for querying OpenTelemetry traces from LLM applications with Opentelemetry support
Listed on its own since 2026-09-25. Until then it was part of iflow-mcp-traceloop-opentelemetry-mcp-server, where its earlier checks remain.
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# OpenTelemetry MCP Server [](https://www.python.org/downloads/) [](https://pypi.org/project/opentelemetry-mcp/) [](LICENSE) **Query and analyze LLM traces with AI assistance.** Ask Claude to find expensive API calls, debug errors, compare model performance, or track token usage—all from your IDE. An MCP (Model Context Protocol) server that connects AI assistants to OpenTelemetry trace backends (Jaeger, Tempo, Traceloop), with specialized support for LLM observability through OpenLLMetry semantic conventions. **See it in action:** https://github.com/user-attachments/assets/e2106ef9-0a58-4ba0-8b2b-e114c0b8b4b9 --- ## Table of Contents - [Quick Start](#quick-start) - [Installation](#installation) - [Features](#features) - [Configuration](#configuration) - [Tools Reference](#tools-reference) - [Example Queries](#example-queries) - [Common Workflows](#common-workflows) - [Troubleshooting](#troubleshooting) - [Development](#development) - [Support](#support) --- ## Quick Start **No installation required!** Configure your client to run the server directly from PyPI: ```json // Add to claude_desktop_config.json: { "mcpServers": { "opentelemetry-mcp": { "command": "pipx", "args": ["run", "opentelemetry-mcp"], "env": { "BACKEND_TYPE": "jaeger", "BACKEND_URL": "http://localhost:16686" } } } } ``` Or use `uvx` (alternative): ```json { "mcpServers": { "opentelemetry-mcp": { "command": "uvx", "args": ["opentelemetry-mcp"], "env": { "BACKEND_TYPE": "jaeger", "BACKEND_URL": "http://localhost:16686" } } } } ``` **That's it!** Ask Claude: _"Show me traces with errors from the last hour"_ --- ## Installation ### For End Users (Recommended) ```bash # Run without installing…
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