by z9905080
Monitor LLM performance with Langfuse: advanced ai data analytics and data analysis ai for actionable insights and impro
Connects AI models to Langfuse analytics workspaces for querying LLM performance metrics. Requires Langfuse project setup with public/private keys.
Langfuse is a community-built MCP server published by z9905080 that provides AI assistants with tools and capabilities via the Model Context Protocol. Monitor LLM performance with Langfuse: advanced ai data analytics and data analysis ai for actionable insights and impro It is categorized under ai ml, analytics data.
You can install Langfuse in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
Apache-2.0
Langfuse is released under the Apache-2.0 license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
Langfuse is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Langfuse is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
We wired Langfuse into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
We evaluated Langfuse against two servers with overlapping tools; this profile had the clearer scope statement.
Langfuse is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
We evaluated Langfuse against two servers with overlapping tools; this profile had the clearer scope statement.
Langfuse has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Langfuse is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
We evaluated Langfuse against two servers with overlapping tools; this profile had the clearer scope statement.
We evaluated Langfuse against two servers with overlapping tools; this profile had the clearer scope statement.
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A Model Context Protocol (MCP) server implementation for integrating AI assistants with Langfuse workspaces.
This package provides an MCP server that enables AI assistants to interact with Langfuse workspaces. It allows AI models to:
# Install from npm
npm install shouting-mcp-langfuse
# Or install globally
npm install -g shouting-mcp-langfuse
You can find the package on npm: shouting-mcp-langfuse
Before using the server, you need to create a Langfuse project and obtain your project's public and private keys. You can find these keys in the Langfuse dashboard.
The server requires the following environment variables:
LANGFUSE_DOMAIN: The Langfuse domain (default: https://api.langfuse.com)LANGFUSE_PUBLIC_KEY: Your Langfuse Project Public KeyLANGFUSE_PRIVATE_KEY: Your Langfuse Project Private Key# Set environment variables
export LANGFUSE_DOMAIN="https://api.langfuse.com"
export LANGFUSE_PUBLIC_KEY="your-public-key"
export LANGFUSE_PRIVATE_KEY="your-private
# Run the server
mcp-server-langfuse
import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { langfuseClient } from "shouting-mcp-langfuse";
// Initialize the server and client
const server = new Server({...});
const langfuseClient = new LangfuseClient(process.env.LANGFUSE_DOMAIN, process.env.LANGFUSE_PUBLIC_KEY, process.env.LANGFUSE_PRIVATE_KEY);
// Register your custom handlers
// ...
The server provides the following langfuse integration tools:
getLLMMetricsByTimeRange: Get LLM Metrics by Time RangeISC
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.