Langfuse (LLM Tracing & Evals) MCP Server for Claude Code 10 tools — connect in under 2 minutes
Claude Code is Anthropic's agentic CLI for terminal-first development. Add Langfuse (LLM Tracing & Evals) as an MCP server in one command and Claude Code will discover every tool at runtime — ideal for automation pipelines, CI/CD integration, and headless workflows via the Vinkius.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
Vinkius Desktop App
The modern way to manage MCP Servers — no config files, no terminal commands. Install Langfuse (LLM Tracing & Evals) and 2,500+ MCP Servers from a single visual interface.
# Your Vinkius token — get it at cloud.vinkius.com
claude mcp add langfuse-llm-tracing-evals --transport http "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Langfuse (LLM Tracing & Evals) MCP Server
Connect your Langfuse account to any AI agent and take full control of your LLM observability, prompt management, and quality evaluation through natural conversation.
Claude Code registers Langfuse (LLM Tracing & Evals) as an MCP server in a single terminal command. Once connected, Claude Code discovers all 10 tools at runtime and can call them headlessly — ideal for CI/CD pipelines, cron jobs, and automated workflows where Langfuse (LLM Tracing & Evals) data drives decisions without human intervention.
What you can do
- Trace Orchestration — List and retrieve detailed traces of LLM API sessions, exposing latencies, token counts, and exact chained payloads directly from your agent
- Prompt Vault Access — Query actively managed prompt templates and versions to inspect system instructions and expected input variables
- Observation Analysis — Deep-dive into individual spans, events, and generations within a trace to pinpoint failures or performance bottlenecks securely
- Evaluation & Scoring — Attach structured human feedback or automated evaluation metrics to specific traces to monitor model grounding and accuracy
- Usage Metrics — Generate aggregated daily reports on USD costs and average latency to track your AI infrastructure spending in real-time
- Session Monitoring — Extract correlated user sessions to understand multi-turn interaction boundaries and improve long-term agentic workflows
The Langfuse (LLM Tracing & Evals) MCP Server exposes 10 tools through the Vinkius. Connect it to Claude Code in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Langfuse (LLM Tracing & Evals) to Claude Code via MCP
Follow these steps to integrate the Langfuse (LLM Tracing & Evals) MCP Server with Claude Code.
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
Add the MCP Server
Run the command above in your terminal
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
Start using Langfuse (LLM Tracing & Evals)
Ask Claude: "Using Langfuse (LLM Tracing & Evals), show me..." — 10 tools are ready
Why Use Claude Code with the Langfuse (LLM Tracing & Evals) MCP Server
Claude Code provides unique advantages when paired with Langfuse (LLM Tracing & Evals) through the Model Context Protocol.
Single-command setup: `claude mcp add` registers the server instantly — no config files to edit or applications to restart
Terminal-native workflow means MCP tools integrate seamlessly into shell scripts, CI/CD pipelines, and automated DevOps tasks
Claude Code runs headlessly, enabling unattended batch processing using Langfuse (LLM Tracing & Evals) tools in cron jobs or deployment scripts
Built by the same team that created the MCP protocol, ensuring first-class compatibility and the fastest adoption of new protocol features
Langfuse (LLM Tracing & Evals) + Claude Code Use Cases
Practical scenarios where Claude Code combined with the Langfuse (LLM Tracing & Evals) MCP Server delivers measurable value.
CI/CD integration: embed Langfuse (LLM Tracing & Evals) tool calls in your deployment pipeline to validate configurations or fetch secrets before shipping
Headless batch processing: schedule Claude Code to query Langfuse (LLM Tracing & Evals) nightly and generate reports without human intervention
Shell scripting: pipe Langfuse (LLM Tracing & Evals) outputs into other CLI tools for data transformation, filtering, and aggregation
Infrastructure monitoring: run Claude Code in a cron job to query Langfuse (LLM Tracing & Evals) status endpoints and alert on anomalies
Langfuse (LLM Tracing & Evals) MCP Tools for Claude Code (10)
These 10 tools become available when you connect Langfuse (LLM Tracing & Evals) to Claude Code via MCP:
create_observation
Create a new LLM observation (span, event, generation) inside a trace
create_score
g. 1-5 stars) or automated pipeline metrics bounding exactly onto the specified Trace or Observation. Attach human feedback or evaluation metrics to a trace/observation
get_daily_metrics
Generate rolled-up USD cost and aggregated latency statistics
get_observation
Retrieve explicit span or generation context within a trace
get_trace
Get complete telemetry and nested graph for a single trace
list_observations
List raw observation objects spanning across traces
list_prompts
Extract actively managed prompt templates and versions
list_scores
List all explicit scores mapping quality or cost algorithms
list_sessions
List high-level user session entities encapsulating multiple traces
list_traces
List all traces tracking LLM API sessions
Example Prompts for Langfuse (LLM Tracing & Evals) in Claude Code
Ready-to-use prompts you can give your Claude Code agent to start working with Langfuse (LLM Tracing & Evals) immediately.
"List the last 5 traces in my Langfuse project"
"Show me the instructions for the 'customer-support-v3' prompt"
"What was our total LLM spending for today?"
Troubleshooting Langfuse (LLM Tracing & Evals) MCP Server with Claude Code
Common issues when connecting Langfuse (LLM Tracing & Evals) to Claude Code through the Vinkius, and how to resolve them.
Command not found: claude
npm install -g @anthropic-ai/claude-codeConnection timeout
Langfuse (LLM Tracing & Evals) + Claude Code FAQ
Common questions about integrating Langfuse (LLM Tracing & Evals) MCP Server with Claude Code.
How do I add an MCP server to Claude Code?
claude mcp add --transport http "" in your terminal. Claude Code registers the server and discovers all tools immediately.Can Claude Code run MCP tools in headless mode?
How do I list all connected MCP servers?
claude mcp in your terminal to see all registered servers and their status, or type /mcp inside an active Claude Code session.Connect Langfuse (LLM Tracing & Evals) with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Langfuse (LLM Tracing & Evals) to Claude Code
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.