langfuse-mcp

Langfuse MCP server enables AI agents to query Langfuse trace data for enhanced debugging and observability. Operations teams benefit from improved AI agent performance monitoring and troubleshooting. Integrates with Langfuse for trace data analysis and supports Claude agents.

[{"step":"Set up the Langfuse MCP server and authenticate it with your Langfuse project. Use the server’s configuration to specify the project ID and API key.","action":"Run `langfuse-mcp-server --project-id your_project_id --api-key your_api_key` in your terminal or configure it in your agent’s environment."},{"step":"Identify the agent or trace you want to analyze. Use placeholders like [AGENT_NAME] or [TIME_PERIOD] in your prompt to scope the query.","action":"Replace [AGENT_NAME] with the exact name of your agent (e.g., 'CustomerSupportAgent') and [TIME_PERIOD] with a duration (e.g., 'last 3 days')."},{"step":"Define the metrics or thresholds to focus on. Common examples include latency, error rates, or tool call failures.","action":"Specify [SPECIFIC_METRIC] (e.g., 'latency') and [THRESHOLD] (e.g., '5 seconds') to filter traces automatically."},{"step":"Review the AI’s output and extract actionable insights. Cross-reference the trace data with your agent’s logs or code to validate findings.","action":"Use the suggested fixes to update your agent’s configuration, tooling, or prompts. Test the changes in a staging environment before deploying to production."},{"step":"Monitor the agent’s performance post-fix using the same Langfuse queries to confirm improvements.","action":"Re-run the query after 24-48 hours to compare latency, error rates, and user satisfaction metrics. Adjust thresholds or queries as needed."}]