ai-mlanalytics-data

Langfuse

z9905080

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, enabling access to LLM performance metrics by time range for monitoring and analysis.

github stars

3

0 commentsdiscussion

Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Direct Langfuse workspace integrationTime-based metrics queries

best for

  • / AI developers monitoring model performance
  • / Teams tracking LLM usage analytics
  • / Analyzing AI assistant effectiveness over time

capabilities

  • / Query LLM metrics by time range
  • / Connect to Langfuse workspaces
  • / Access performance analytics data
  • / Retrieve model execution metrics

what it does

Connects AI models to Langfuse analytics workspaces for querying LLM performance metrics. Requires Langfuse project setup with public/private keys.

about

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.

how to install

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.

license

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.

readme

MCP Server for langfuse

npm version

A Model Context Protocol (MCP) server implementation for integrating AI assistants with Langfuse workspaces.

Overview

This package provides an MCP server that enables AI assistants to interact with Langfuse workspaces. It allows AI models to:

  • Query LLM Metrics by Time Range

Installation

# 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

Prerequisites

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.

  1. set up a Langfuse project
  2. get the public and private keys
  3. set the environment variables

Configuration

The server requires the following environment variables:

  • LANGFUSE_DOMAIN: The Langfuse domain (default: https://api.langfuse.com)
  • LANGFUSE_PUBLIC_KEY: Your Langfuse Project Public Key
  • LANGFUSE_PRIVATE_KEY: Your Langfuse Project Private Key

Usage

Running as a CLI Tool

# 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

Using in Your Code

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
// ...

Available Tools

The server provides the following langfuse integration tools:

  • getLLMMetricsByTimeRange: Get LLM Metrics by Time Range

License

ISC

Author

[email protected]

Repository

https://github.com/z9905080/mcp-langfuse

FAQ

What is the Langfuse MCP server?
Langfuse is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Langfuse?
This profile displays 46 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.8 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

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

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

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

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ 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.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.846 reviews
  • Fatima Anderson· Dec 16, 2024

    Langfuse is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Shikha Mishra· Dec 8, 2024

    Langfuse is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Arya Bansal· Dec 8, 2024

    We wired Langfuse into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Evelyn Malhotra· Dec 4, 2024

    We evaluated Langfuse against two servers with overlapping tools; this profile had the clearer scope statement.

  • Yash Thakker· Nov 27, 2024

    Langfuse is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Kwame Park· Nov 27, 2024

    We evaluated Langfuse against two servers with overlapping tools; this profile had the clearer scope statement.

  • Sakshi Patil· Nov 23, 2024

    Langfuse has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Michael Reddy· Nov 7, 2024

    Langfuse is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Michael Anderson· Oct 26, 2024

    We evaluated Langfuse against two servers with overlapping tools; this profile had the clearer scope statement.

  • Dhruvi Jain· Oct 18, 2024

    We evaluated Langfuse against two servers with overlapping tools; this profile had the clearer scope statement.

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