The Tempo MCP Server allows AI assistants to query and analyze distributed tracing data from Grafana Tempo by implementing the Model Context Protocol (MCP). It supports multiple communication modes, including stdin/stdout and HTTP SSE, making it suitable for integration with tools like Claude Desktop, Cursor, and n8n. The server provides robust tools for real-time trace data retrieval, facilitating performance monitoring and debugging workflows.
Added on:
Created by:
Apr 19 2025
The Tempo MCP Server allows AI assistants to query and analyze distributed tracing data from Grafana Tempo by implementing the Model Context Protocol (MCP). It supports multiple communication modes, including stdin/stdout and HTTP SSE, making it suitable for integration with tools like Claude Desktop, Cursor, and n8n. The server provides robust tools for real-time trace data retrieval, facilitating performance monitoring and debugging workflows.
Added on:
Created by:
Apr 19 2025
Scott Lepper
Featured
What is Tempo MCP Server?
The Tempo MCP Server is a comprehensive implementation of the Model Context Protocol (MCP) designed specifically for Grafana Tempo. It enables AI assistants to perform complex queries on distributed tracing data, supporting flexible authentication and environment configurations. Built with Go, it offers both protocol message-based communication and HTTP-based real-time streaming via Server-Sent Events (SSE). Key features include a versatile Tempo query tool, Docker support for easy deployment, and integration with productivity tools like Claude Desktop, Cursor, and n8n for enhanced trace analysis. This server significantly streamlines observability workflows by providing automated and interactive access to trace data, improving debugging and system optimization processes.
Who will use Tempo MCP Server?
- AI developers
- DevOps engineers
- System observability teams
- Performance analysts
- Site reliability engineers
How to use the Tempo MCP Server?
- Step1: Build the server or deploy via Docker
- Step2: Configure environment variables like TEMPO_URL
- Step3: Run the server using the command provided in the documentation
- Step4: Connect client tools such as Claude Desktop, Cursor, or n8n to the server endpoints
- Step5: Perform trace queries using predefined tools or custom query strings
- Step6: Analyze the returned tracing data for system monitoring and debugging
Tempo MCP Server's Core Features & Benefits
The Core Features
- Tempo query tool for retrieving trace data
- Multiple communication modes including stdin/stdout and HTTP SSE
- Docker and Docker Compose support for deployment
- Flexible authentication options (basic, token, environment variables)
- Integration capabilities with Claude Desktop, Cursor, and n8n
The Benefits
- Enables automated and AI-driven trace analysis
- Supports real-time streaming for live monitoring
- Simplifies deployment with container support
- Enhances observability workflows with flexible integrations
- Facilitates performance diagnostics and troubleshooting
Tempo MCP Server's Main Use Cases & Applications
- Automated distributed trace querying in CI/CD pipelines
- Real-time performance monitoring dashboards
- Debugging distributed systems with AI assistance
- Integrating Grafana Tempo tracing data with productivity tools
- Enhancing system observability for cloud-native architectures
FAQs of Tempo MCP Server
Developer
- scottlepp
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