mcp-use Integration with Langfuse

mcp-use is a platform that implements the Model Context Protocol (MCP), an open standard developed by Anthropic for connecting AI agents to external data sources and tools. With Langfuse integration, you can monitor and trace your mcp-use agent interactions for enhanced observability.

What is MCP (Model Context Protocol)? MCP is an open standard that enables AI agents to securely connect to external data sources and tools. It standardizes how AI applications communicate with external services, making it easier to build context-aware AI agents that can interact with databases, APIs, and other tools.

What is mcp-use? mcp-use is a platform that implements the Model Context Protocol, providing developers with tools to build and deploy AI agents that can seamlessly interact with external systems while maintaining proper governance and security.

Integration

Prerequisites

An mcp-use account and configured agents
A Langfuse project (sign up at cloud.langfuse.com or self-host)

Setup Langfuse Integration

Get your Langfuse API keys from your Langfuse project settings page.

Configure Langfuse in mcp-use by navigating to your mcp-use platform settings and adding your Langfuse credentials in the observability section:

# Environment variables for mcp-use
LANGFUSE_PUBLIC_KEY=pk-lf-...
LANGFUSE_SECRET_KEY=sk-lf-...
LANGFUSE_HOST=https://cloud.langfuse.com  # or your self-hosted URL

Enable observability in your mcp-use configuration to start sending traces to Langfuse.

What Gets Traced

With the Langfuse integration enabled, mcp-use automatically captures:

Agent Interactions: Complete conversations and agent decision-making processes
Tool Executions: Detailed traces of external tool calls and their results
Context Usage: How context is managed and utilized across interactions
Performance Metrics: Latency, token usage, and execution times
Error Handling: Failed requests and error recovery processes

Viewing Traces in Langfuse

Once configured, you’ll see detailed traces in your Langfuse dashboard that include:

MCP Protocol Messages: Request/response patterns between agents and tools
Tool Call Chains: Visualization of how agents invoke external tools
Context Flow: How context is passed and maintained across interactions
Resource Access: Details about external data sources and API calls

Example trace structure:

Agent Request
├── Context Preparation
├── Tool Discovery
├── External Tool Call
│   ├── API Request
│   └── Response Processing
└── Agent Response

Benefits

The mcp-use and Langfuse integration provides:

Complete Observability: Full visibility into agent behavior and tool usage
Performance Optimization: Identify bottlenecks in tool execution and context handling
Debugging Support: Trace failures and understand agent decision-making
Security Monitoring: Track data access patterns and tool permissions
Cost Management: Monitor token usage and API call patterns

Configuration Options

Advanced Settings

You can customize the integration with additional configuration options:

// Example configuration for enhanced tracing
{
  "observability": {
    "langfuse": {
      "enabled": true,
      "traceLevel": "detailed", // basic, detailed, verbose
      "captureToolInputs": true,
      "captureToolOutputs": true,
      "captureContext": true,
      "filterSensitiveData": true
    }
  }
}

Metadata Enrichment

mcp-use can automatically enrich traces with metadata about:

Agent configurations and capabilities
Tool permissions and access levels
Context sources and data lineage
User sessions and interaction patterns

Best Practices

Monitor Tool Performance: Use Langfuse dashboards to track tool execution times and success rates
Analyze Context Usage: Understand how context affects agent performance and accuracy
Set Up Alerts: Configure alerts for failed tool executions or performance degradation
Review Security Patterns: Regularly audit tool access patterns and data usage
Optimize Workflows: Use trace data to identify and eliminate unnecessary tool calls

Troubleshooting

If traces aren’t appearing in Langfuse:

Verify Credentials: Ensure your Langfuse API keys are correct and have proper permissions
Check Network: Confirm mcp-use can reach your Langfuse instance (firewall/proxy settings)
Review Configuration: Validate that observability is enabled in your mcp-use settings
Check Logs: Review mcp-use logs for any integration errors or warnings

Learn More

Inferable Milvus

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