Langfuse (LLM Tracing & Evals) MCP Server for Cursor 10 tools — connect in under 2 minutes
Cursor is an AI-first code editor built on VS Code that integrates LLM-powered coding assistance directly into the development workflow. Its Agent mode enables autonomous multi-step coding tasks, and MCP support lets agents access external data sources and APIs during code generation.
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{
"mcpServers": {
"langfuse-llm-tracing-evals": {
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}
}
}
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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.
Cursor's Agent mode turns Langfuse (LLM Tracing & Evals) into an in-editor superpower. Ask Cursor to generate code using live data from Langfuse (LLM Tracing & Evals) and it fetches, processes, and writes — all in a single agentic loop. 10 tools appear alongside file editing and terminal access, creating a unified development environment grounded in real-time information.
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 Cursor 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 Cursor via MCP
Follow these steps to integrate the Langfuse (LLM Tracing & Evals) MCP Server with Cursor.
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
Save the file
Cursor will automatically detect the new MCP server
Start using Langfuse (LLM Tracing & Evals)
Open Agent mode in chat and ask: "Using Langfuse (LLM Tracing & Evals), help me..." — 10 tools available
Why Use Cursor with the Langfuse (LLM Tracing & Evals) MCP Server
Cursor AI Code Editor provides unique advantages when paired with Langfuse (LLM Tracing & Evals) through the Model Context Protocol.
Agent mode turns Cursor into an autonomous coding assistant that can read files, run commands, and call MCP tools without switching context
Cursor's Composer feature can generate entire files using real-time data fetched through MCP — no copy-pasting from external dashboards
MCP tools appear alongside built-in tools like file reading and terminal access, creating a unified agentic environment
VS Code extension compatibility means your existing workflow, keybindings, and extensions all work alongside MCP tools
Langfuse (LLM Tracing & Evals) + Cursor Use Cases
Practical scenarios where Cursor combined with the Langfuse (LLM Tracing & Evals) MCP Server delivers measurable value.
Code generation with live data: ask Cursor to generate a security report module using live DNS and subdomain data fetched through MCP
Automated documentation: have Cursor query your API's tool schemas and generate TypeScript interfaces or OpenAPI specs automatically
Infrastructure-as-code: Cursor can fetch domain configurations and generate corresponding Terraform or CloudFormation templates
Test scaffolding: ask Cursor to pull real API responses via MCP and generate unit test fixtures from actual data
Langfuse (LLM Tracing & Evals) MCP Tools for Cursor (10)
These 10 tools become available when you connect Langfuse (LLM Tracing & Evals) to Cursor 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 Cursor
Ready-to-use prompts you can give your Cursor 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 Cursor
Common issues when connecting Langfuse (LLM Tracing & Evals) to Cursor through the Vinkius, and how to resolve them.
Tools not appearing in Cursor
Server shows as disconnected
Langfuse (LLM Tracing & Evals) + Cursor FAQ
Common questions about integrating Langfuse (LLM Tracing & Evals) MCP Server with Cursor.
What is Agent mode and why does it matter for MCP?
Where does Cursor store MCP configuration?
mcp.json file. You can configure servers at the project level (.cursor/mcp.json in your project root) or globally (~/.cursor/mcp.json). Project-level configs take precedence.Can Cursor use MCP tools in inline edits?
How do I verify MCP tools are loaded?
Connect Langfuse (LLM Tracing & Evals) with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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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 Cursor
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.