Langfuse (LLM Tracing & Evals) MCP Server for Google ADK 10 tools — connect in under 2 minutes
Google Agent Development Kit (ADK) is Google's framework for building production AI agents. Add Langfuse (LLM Tracing & Evals) as an MCP tool provider through the Vinkius and your ADK agents can call every tool with full schema introspection.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import (
StreamableHTTPConnectionParams,
)
# Your Vinkius token — get it at cloud.vinkius.com
mcp_tools = McpToolset(
connection_params=StreamableHTTPConnectionParams(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
)
)
agent = Agent(
model="gemini-2.5-pro",
name="langfuse_llm_tracing_evals_agent",
instruction=(
"You help users interact with Langfuse (LLM Tracing & Evals) "
"using 10 available tools."
),
tools=[mcp_tools],
)
* 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.
Google ADK natively supports Langfuse (LLM Tracing & Evals) as an MCP tool provider — declare the Vinkius Edge URL and the framework handles discovery, validation, and execution automatically. Combine 10 tools with Gemini's long-context reasoning for complex multi-tool workflows, with production-ready session management and evaluation built in.
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 Google ADK 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 Google ADK via MCP
Follow these steps to integrate the Langfuse (LLM Tracing & Evals) MCP Server with Google ADK.
Install Google ADK
Run pip install google-adk
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Create the agent
Save the code above and integrate into your ADK workflow
Explore tools
The agent will discover 10 tools from Langfuse (LLM Tracing & Evals) via MCP
Why Use Google ADK with the Langfuse (LLM Tracing & Evals) MCP Server
Google ADK provides unique advantages when paired with Langfuse (LLM Tracing & Evals) through the Model Context Protocol.
Google ADK natively supports MCP tool servers — declare a tool provider and the framework handles discovery, validation, and execution
Built on Gemini models, ADK provides long-context reasoning ideal for complex multi-tool workflows with Langfuse (LLM Tracing & Evals)
Production-ready features like session management, evaluation, and deployment come built-in — not bolted on
Seamless integration with Google Cloud services means you can combine Langfuse (LLM Tracing & Evals) tools with BigQuery, Vertex AI, and Cloud Functions
Langfuse (LLM Tracing & Evals) + Google ADK Use Cases
Practical scenarios where Google ADK combined with the Langfuse (LLM Tracing & Evals) MCP Server delivers measurable value.
Enterprise data agents: ADK agents query Langfuse (LLM Tracing & Evals) and cross-reference results with internal databases for comprehensive analysis
Multi-modal workflows: combine Langfuse (LLM Tracing & Evals) tool responses with Gemini's vision and language capabilities in a single agent
Automated compliance checks: schedule ADK agents to query Langfuse (LLM Tracing & Evals) regularly and flag policy violations or configuration drift
Internal tool platforms: build self-service agent platforms where teams connect their own MCP servers including Langfuse (LLM Tracing & Evals)
Langfuse (LLM Tracing & Evals) MCP Tools for Google ADK (10)
These 10 tools become available when you connect Langfuse (LLM Tracing & Evals) to Google ADK 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 Google ADK
Ready-to-use prompts you can give your Google ADK 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 Google ADK
Common issues when connecting Langfuse (LLM Tracing & Evals) to Google ADK through the Vinkius, and how to resolve them.
McpToolset not found
pip install --upgrade google-adkLangfuse (LLM Tracing & Evals) + Google ADK FAQ
Common questions about integrating Langfuse (LLM Tracing & Evals) MCP Server with Google ADK.
How does Google ADK connect to MCP servers?
Can ADK agents use multiple MCP servers?
Which Gemini models work best with MCP tools?
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 Google ADK
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.