Langfuse (LLM Tracing & Evals) MCP Server
Monitor LLM apps via Langfuse — track traces, manage prompt templates, and audit evaluation scores.
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What is the Langfuse MCP Server?
The Langfuse MCP Server gives AI agents like Claude, ChatGPT, and Cursor direct access to Langfuse via 10 tools. Monitor LLM apps via Langfuse — track traces, manage prompt templates, and audit evaluation scores. Powered by the Vinkius - no API keys, no infrastructure, connect in under 2 minutes.
Built-in capabilities (10)
Tools for your AI Agents to operate Langfuse
Ask your AI agent "List the last 5 traces in my Langfuse project" and get the answer without opening a single dashboard. With 10 tools connected to real Langfuse data, your agents reason over live information, cross-reference it with other MCP servers, and deliver insights you would spend hours assembling manually.
Works with Claude, ChatGPT, Cursor, and any MCP-compatible client. Powered by the Vinkius - your credentials never touch the AI model, every request is auditable. Connect in under two minutes.
Why teams choose Vinkius
One subscription gives you access to thousands of MCP servers - and you can deploy your own to the Vinkius Edge. Your AI agents only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure and security, zero maintenance.
Build your own MCP Server with our secure development framework →Vinkius works with every AI agent you already use
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Langfuse (LLM Tracing & Evals) MCP Server capabilities
10 toolsCreate a new LLM observation (span, event, generation) inside a trace
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
Generate rolled-up USD cost and aggregated latency statistics
Retrieve explicit span or generation context within a trace
Get complete telemetry and nested graph for a single trace
List raw observation objects spanning across traces
Extract actively managed prompt templates and versions
List all explicit scores mapping quality or cost algorithms
List high-level user session entities encapsulating multiple traces
List all traces tracking LLM API sessions
What the Langfuse (LLM Tracing & Evals) MCP Server unlocks
Connect your Langfuse account to any AI agent and take full control of your LLM observability, prompt management, and quality evaluation through natural conversation.
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
How it works
1. Subscribe to this server
2. Enter your Langfuse API URL, Public Key, and Secret Key
3. Start monitoring your LLM application from Claude, Cursor, or any MCP-compatible client
Who is this for?
- LLM Engineers — debug complex AI chains and measure exact token latencies through natural conversation without manual dashboard searching
- Product Owners — monitor daily AI costs and user satisfaction scores across multiple production environments
- Data Scientists — manage prompt templates and audit evaluation metrics to improve model response quality and grounding efficiently
Frequently asked questions about the Langfuse (LLM Tracing & Evals) MCP Server
Can I see the exact system instruction for a specific prompt version?
Yes. Use the list_prompts tool to browse your managed templates. Your agent can retrieve the exact text and variables for any deployed prompt version, making it easy to audit AI logic through natural conversation.
How do I log human feedback for a specific trace?
Use the create_score tool by providing the Trace ID and a JSON payload defining the score name (e.g. 'user-satisfaction') and value. Your agent will attach this structured data directly to the Langfuse record.
Can my agent report on my LLM spending for the current day?
Absolutely. The get_daily_metrics tool retrieves aggregated USD costs and average latency metrics from Langfuse. Your agent can summarize these statistics to help you monitor your infrastructure budget in real-time.
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Give your AI agents the power of Langfuse MCP Server
Production-grade Langfuse (LLM Tracing & Evals) MCP Server. Verified, monitored, and maintained by Vinkius. Ready for your AI agents — connect and start using immediately.






