Tempo MCP Server Overview

The Tempo MCP Server bridges AI assistants with Grafana Tempo, the distributed tracing backend widely used in observability stacks. By exposing a Model Context Protocol (MCP) interface, it lets tools such as Claude Desktop send structured queries to Tempo and receive trace data in real time. For developers building AI‑powered debugging or monitoring workflows, this server eliminates the need to write custom integrations for Tempo, providing a ready‑made bridge that respects MCP’s declarative tool contract.

At its core, the server implements two communication channels: a standard stdin/stdout MCP stream and an HTTP service that supports Server‑Sent Events (SSE). The SSE endpoint () streams trace results as they arrive, enabling low‑latency, push‑style updates that are ideal for live dashboards or conversational agents that need to present evolving trace information. The MCP endpoint () remains the primary entry point for clients that issue explicit tool calls, keeping the interaction deterministic and stateless.

The main tool exposed by Tempo MCP is . It accepts a Tempo query string (e.g., or ) along with optional parameters such as time range, result limit, and authentication credentials. Environment variables (, ) provide sensible defaults while still allowing per‑request overrides. This flexibility lets developers embed Tempo queries directly into AI prompts, enabling agents to ask for recent traces of a service or to filter by duration without leaving the conversational context.

Typical use cases include:

• AI‑driven incident response: An assistant can fetch the latest traces for a failing service, highlight latency spikes, and suggest remediation steps.
• Observability chatbots: Users can query Tempo from a chat interface, receiving real‑time trace streams that update as new events arrive.
• Automated monitoring: Integrate with workflow tools like n8n via the SSE endpoint to trigger alerts when trace patterns exceed thresholds.

Because Tempo MCP follows the standard MCP contract, it plugs seamlessly into any AI workflow that understands tool calls. Developers can configure Claude Desktop or other MCP‑compatible clients to auto‑approve the tool, ensuring that trace data is fetched only when explicitly requested. The server’s Go implementation guarantees high performance and low overhead, making it suitable for production deployments in observability pipelines.