Langfuse (LLM Tracing & Evals) MCP Server for VS Code Copilot 10 tools — connect in under 2 minutes
GitHub Copilot in VS Code is the most widely adopted AI coding assistant, embedded directly into the world's most popular code editor. With MCP support in Agent mode, Copilot can access external data and APIs to generate context-aware code grounded in real-time information.
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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.
GitHub Copilot Agent mode brings Langfuse (LLM Tracing & Evals) data directly into your VS Code workflow. With a project-scoped config, the entire team shares access to 10 tools — Copilot queries live data, generates typed code, and writes tests from actual API responses, all without leaving the 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 VS Code Copilot 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 VS Code Copilot via MCP
Follow these steps to integrate the Langfuse (LLM Tracing & Evals) MCP Server with VS Code Copilot.
Create MCP config
Create a .vscode/mcp.json file in your project root
Add the server config
Paste the JSON configuration above
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
Start using Langfuse (LLM Tracing & Evals)
Ask Copilot: "Using Langfuse (LLM Tracing & Evals), help me..." — 10 tools available
Why Use VS Code Copilot with the Langfuse (LLM Tracing & Evals) MCP Server
GitHub Copilot for Visual Studio Code provides unique advantages when paired with Langfuse (LLM Tracing & Evals) through the Model Context Protocol.
VS Code is used by over 70% of developers — adding MCP tools to Copilot means your team can leverage external data without leaving their primary editor
Project-scoped MCP configs (`.vscode/mcp.json`) let you commit server configurations to your repository, ensuring the entire team shares the same tool access
Copilot's Agent mode integrates MCP tools seamlessly with file editing, terminal commands, and workspace search in a single agentic loop
GitHub's enterprise compliance and audit features extend to MCP tool usage, providing visibility into how AI interacts with external services
Langfuse (LLM Tracing & Evals) + VS Code Copilot Use Cases
Practical scenarios where VS Code Copilot combined with the Langfuse (LLM Tracing & Evals) MCP Server delivers measurable value.
Live API integration: Copilot can query an MCP server, inspect the response schema, and generate typed API client code in the same step
DevSecOps workflows: security teams can give developers access to domain intelligence tools directly in their editor for real-time vulnerability assessment during code review
Data pipeline development: Copilot fetches sample data via MCP and generates transformation scripts, validators, and test fixtures from actual API responses
Documentation generation: Copilot queries available tools and auto-generates README sections, API reference docs, and usage examples
Langfuse (LLM Tracing & Evals) MCP Tools for VS Code Copilot (10)
These 10 tools become available when you connect Langfuse (LLM Tracing & Evals) to VS Code Copilot 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 VS Code Copilot
Ready-to-use prompts you can give your VS Code Copilot 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 VS Code Copilot
Common issues when connecting Langfuse (LLM Tracing & Evals) to VS Code Copilot through the Vinkius, and how to resolve them.
MCP tools not available
Langfuse (LLM Tracing & Evals) + VS Code Copilot FAQ
Common questions about integrating Langfuse (LLM Tracing & Evals) MCP Server with VS Code Copilot.
Which VS Code version supports MCP?
How do I switch to Agent mode?
Can I restrict which MCP tools Copilot can access?
Does MCP work in VS Code Remote or Codespaces?
.vscode/mcp.json work in Remote SSH, WSL, and GitHub Codespaces environments. The MCP connection is established from the remote host, so ensure the server URL is accessible from that environment.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 VS Code Copilot
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.