Langfuse (LLM Tracing & Evals) MCP Server for Windsurf 10 tools — connect in under 2 minutes
Windsurf brings agentic AI coding to a purpose-built IDE. Connect Langfuse (LLM Tracing & Evals) through the Vinkius and Cascade will auto-discover every tool — ask questions, generate code, and act on live data without leaving your editor.
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{
"mcpServers": {
"langfuse-llm-tracing-evals": {
"url": "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.
Windsurf's Cascade agent chains multiple Langfuse (LLM Tracing & Evals) tool calls autonomously — query data, analyze results, and generate code in a single agentic session. Paste the Vinkius Edge URL, reload, and all 10 tools are immediately available. Real-time tool feedback appears inline, so you see API responses directly in your editor.
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 Windsurf 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 Windsurf via MCP
Follow these steps to integrate the Langfuse (LLM Tracing & Evals) MCP Server with Windsurf.
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
Add the server
Paste the JSON configuration above into mcp_config.json
Save and reload
Windsurf will detect the new server automatically
Start using Langfuse (LLM Tracing & Evals)
Open Cascade and ask: "Using Langfuse (LLM Tracing & Evals), help me..." — 10 tools available
Why Use Windsurf with the Langfuse (LLM Tracing & Evals) MCP Server
Windsurf provides unique advantages when paired with Langfuse (LLM Tracing & Evals) through the Model Context Protocol.
Windsurf's Cascade agent autonomously chains multiple tool calls in sequence, solving complex multi-step tasks without manual intervention
Purpose-built for agentic workflows — Cascade understands context across your entire codebase and integrates MCP tools natively
JSON-based configuration means zero code changes: paste a URL, reload, and all 10 tools are immediately available
Real-time tool feedback is displayed inline, so you see API responses directly in your editor without switching contexts
Langfuse (LLM Tracing & Evals) + Windsurf Use Cases
Practical scenarios where Windsurf combined with the Langfuse (LLM Tracing & Evals) MCP Server delivers measurable value.
Automated code generation: ask Cascade to fetch data from Langfuse (LLM Tracing & Evals) and generate models, types, or handlers based on real API responses
Live debugging: query Langfuse (LLM Tracing & Evals) tools mid-session to inspect production data while debugging without leaving the editor
Documentation generation: pull schema information from Langfuse (LLM Tracing & Evals) and have Cascade generate comprehensive API docs automatically
Rapid prototyping: combine Langfuse (LLM Tracing & Evals) data with Cascade's code generation to scaffold entire features in minutes
Langfuse (LLM Tracing & Evals) MCP Tools for Windsurf (10)
These 10 tools become available when you connect Langfuse (LLM Tracing & Evals) to Windsurf 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 Windsurf
Ready-to-use prompts you can give your Windsurf 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 Windsurf
Common issues when connecting Langfuse (LLM Tracing & Evals) to Windsurf through the Vinkius, and how to resolve them.
Server not connecting
Langfuse (LLM Tracing & Evals) + Windsurf FAQ
Common questions about integrating Langfuse (LLM Tracing & Evals) MCP Server with Windsurf.
How does Windsurf discover MCP tools?
mcp_config.json file on startup and connects to each configured server via Streamable HTTP. Tools are listed in the MCP panel and available to Cascade automatically.Can Cascade chain multiple MCP tool calls?
Does Windsurf support multiple MCP servers?
mcp_config.json. Each server's tools appear in the MCP panel and Cascade can use tools from different servers in a single flow.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 Windsurf
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.