Langfuse (LLM Tracing & Evals) MCP Server for Vercel AI SDK 10 tools — connect in under 2 minutes
The Vercel AI SDK is the TypeScript toolkit for building AI-powered applications. Connect Langfuse (LLM Tracing & Evals) through the Vinkius and every tool is available as a typed function — ready for React Server Components, API routes, or any Node.js backend.
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Vinkius supports streamable HTTP and SSE.
import { createMCPClient } from "@ai-sdk/mcp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";
async function main() {
const mcpClient = await createMCPClient({
transport: {
type: "http",
// Your Vinkius token — get it at cloud.vinkius.com
url: "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
},
});
try {
const tools = await mcpClient.tools();
const { text } = await generateText({
model: openai("gpt-4o"),
tools,
prompt: "Using Langfuse (LLM Tracing & Evals), list all available capabilities.",
});
console.log(text);
} finally {
await mcpClient.close();
}
}
main();
* 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.
The Vercel AI SDK gives every Langfuse (LLM Tracing & Evals) tool full TypeScript type inference, IDE autocomplete, and compile-time error checking. Connect 10 tools through the Vinkius and stream results progressively to React, Svelte, or Vue components — works on Edge Functions, Cloudflare Workers, and any Node.js runtime.
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 Vercel AI SDK 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 Vercel AI SDK via MCP
Follow these steps to integrate the Langfuse (LLM Tracing & Evals) MCP Server with Vercel AI SDK.
Install dependencies
Run npm install @ai-sdk/mcp ai @ai-sdk/openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the script
Save to agent.ts and run with npx tsx agent.ts
Explore tools
The SDK discovers 10 tools from Langfuse (LLM Tracing & Evals) and passes them to the LLM
Why Use Vercel AI SDK with the Langfuse (LLM Tracing & Evals) MCP Server
Vercel AI SDK provides unique advantages when paired with Langfuse (LLM Tracing & Evals) through the Model Context Protocol.
TypeScript-first: every MCP tool gets full type inference, IDE autocomplete, and compile-time error checking out of the box
Framework-agnostic core works with Next.js, Nuxt, SvelteKit, or any Node.js runtime — same Langfuse (LLM Tracing & Evals) integration everywhere
Built-in streaming UI primitives let you display Langfuse (LLM Tracing & Evals) tool results progressively in React, Svelte, or Vue components
Edge-compatible: the AI SDK runs on Vercel Edge Functions, Cloudflare Workers, and other edge runtimes for minimal latency
Langfuse (LLM Tracing & Evals) + Vercel AI SDK Use Cases
Practical scenarios where Vercel AI SDK combined with the Langfuse (LLM Tracing & Evals) MCP Server delivers measurable value.
AI-powered web apps: build dashboards that query Langfuse (LLM Tracing & Evals) in real-time and stream results to the UI with zero loading states
API backends: create serverless endpoints that orchestrate Langfuse (LLM Tracing & Evals) tools and return structured JSON responses to any frontend
Chatbots with tool use: embed Langfuse (LLM Tracing & Evals) capabilities into conversational interfaces with streaming responses and tool call visibility
Internal tools: build admin panels where team members interact with Langfuse (LLM Tracing & Evals) through natural language queries
Langfuse (LLM Tracing & Evals) MCP Tools for Vercel AI SDK (10)
These 10 tools become available when you connect Langfuse (LLM Tracing & Evals) to Vercel AI SDK 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 Vercel AI SDK
Ready-to-use prompts you can give your Vercel AI SDK 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 Vercel AI SDK
Common issues when connecting Langfuse (LLM Tracing & Evals) to Vercel AI SDK through the Vinkius, and how to resolve them.
createMCPClient is not a function
npm install @ai-sdk/mcpLangfuse (LLM Tracing & Evals) + Vercel AI SDK FAQ
Common questions about integrating Langfuse (LLM Tracing & Evals) MCP Server with Vercel AI SDK.
How does the Vercel AI SDK connect to MCP servers?
createMCPClient from @ai-sdk/mcp and pass the server URL. The SDK discovers all tools and provides typed TypeScript interfaces for each one.Can I use MCP tools in Edge Functions?
Does it support streaming tool results?
useChat and streamText that handle tool calls and display results progressively in the UI.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 Vercel AI SDK
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.