langfuse

MCP serverMonitoring & ops

Lets your agent inspect and analyse traces and observations from your Langfuse LLM monitoring data.

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About this server

MCP server for Langfuse LLM observability, trace and observation analysis.

Getting started

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As published by pulsemcp/mcp-servers in README.md. This repository publishes several servers, so only the opening is shown here.

We build reliable servers thoughtfully designed for MCP Client-powered workflows.

You can have confidence that any Pulse-branded MCP server was built with these north stars in mind:

Purpose-built. LLM-powered MCP clients offer uniquely new user interaction patterns that necessitate a different layer of abstraction than the REST API's from a pre-AI era. We thoughtfully scope drawing lines like building a new server, versus incorporating a new feature in another server. Or deciding where one slew of REST API endpoints might be better packaged as a single Tool call. And more.
Easy set up. Many MCP servers die before ever getting a chance to be used. We offer guides and a frustration-free experience to get going with our MCP servers inside your favorite MCP clients.
Time savings. By minimizing the number of tool chain steps or conversational turns you need to accomplish a task, our MCP servers will save you (and your agents) time waiting for a task to be completed.
Inference cost savings. By minimizing the number of tokens you need to consume to a accomplish a task, our MCP servers will save you on your LLM inference bills.
**Relia

Advanced

Delivery: langfuse MCP server → your ahel connector (mcp.ahel.ai) → your AI.
Item type: mcp-server
Key: com-pulsemcp-langfuse
Source: github.com/pulsemcp/mcp-servers