@avivsinai
Basic information
A Model Context Protocol (MCP) server for Langfuse, enabling AI agents to query Langfuse trace data for enhanced debugging and observability
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"langfuse-mcp": {
"command": "npx",
"args": [
"skills",
"add",
"avivsinai/langfuse-mcp",
"-g",
"-y"
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
Fetch the live tool list by running this server in a temporary sandbox using the button above.
Langfuse MCP is an agent-facing Model Context Protocol server and skill for Langfuse observability. It lets AI agents query traces, inspect generations, debug exceptions, analyze sessions, manage prompts, browse datasets, and understand what agents did in production.
Install via uvx langfuse-mcp with Python 3.10+ and the uv runtime. Set environment variables LANGFUSE_PUBLIC_KEY, LANGFUSE_SECRET_KEY, and LANGFUSE_HOST (defaults to Langfuse Cloud). Add the server to an MCP client (Claude Code, Codex, Cursor) using the provided configuration commands or JSON files. Start the MCP client, then invoke tools directly or use the included agent skill for ready‑made debugging playbooks.
--tools flag to reduce token overhead.--read-only) disables all write operations.--default-output-mode) for compact, full JSON string, or file output.Authorization header.Langfuse MCP is a local, debugging‑first server with compact trace inspection, exception triage, session analysis, and route‑decision tools. It does not mirror every native MCP tool; the native MCP is a hosted endpoint with a broader API surface.
Python 3.10–3.14 and the `uv` package runner (for `uvx`). The server is verified on CI for those Python versions.
Yes. Use `langfuse-mcp --tools traces,prompts` to load only the tool groups you need. Available groups include traces, observations, routing, sessions, exceptions, prompts, datasets, annotation_queues, scores, metrics, and schema.
Yes. Start with `langfuse-mcp --read-only` or set `LANGFUSE_MCP_READ_ONLY=true` to disable all write operations (e.g., creating prompts, datasets, annotation queue items).
Run the server in HTTP transport mode with `--transport streamable-http`. Each MCP client passes its project credentials as a `Basic` `Authorization` header (base64(public_key:secret_key)). An absent header falls back to the server’s startup environment credentials.
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