Tempo and AI
Tempo exposes a Model Context Protocol (MCP) server and LLM-optimized API endpoints that let AI agents query traces, compute metrics, and discover attributes directly. Grafana also provides command-line tools that connect agents to the broader observability platform.
Model Context Protocol server
The
MCP server at /api/mcp lets agents search for traces with TraceQL, retrieve a trace by ID, compute metrics from span data, and discover available attributes.
The server also serves TraceQL syntax documentation as MCP resources, so agents can look up query syntax on demand instead of relying on training data.
The MCP server uses the streamable-http transport and supports the same authentication and
multi-tenancy as other Tempo API endpoints.
Warning
The MCP server returns tracing data to the calling agent, which may forward it to an LLM provider. Consider the content of your tracing data and your organization’s policies before connecting agents to this endpoint.
To try it locally, refer to the MCP server quick start.
LLM-optimized API responses
The
trace by ID v2 and
tag values v2 endpoints accept an Accept: application/vnd.grafana.llm header.
This returns a simplified JSON format that strips unnecessary detail and reduces token usage, so agents can process larger traces within their context window.
Caution
The simplified LLM format is subject to change and shouldn’t be relied on for programmatic use.
Use this header for interactive or experimental agent use. For stable integrations, use the MCP server or request the standard JSON or protobuf formats.
Tempo features relevant to AI workflows
These Tempo features aren’t AI-specific, but they directly improve what agents can do with tracing data.
TraceQL metrics at general availability
The MCP server’s traceql-metrics-instant and traceql-metrics-range tools let agents compute rate, error, and duration metrics directly from trace data.
Because
TraceQL metrics moved from experimental to GA in Tempo 3.0, these tools query a production-ready engine.
OR conditions for tag autocomplete
The search tags v2 and search tag values v2 APIs support OR conditions, so autocomplete requests can match multiple values in a single call instead of making separate requests. When an agent discovers available attributes through the MCP tools, fewer round-trips mean faster, cheaper exploration of what’s queryable.
Control what agents can access
Before you open up trace data to agents through the MCP server or API endpoints, you can control what’s available. If your traces contain personally identifiable information or security tokens, use the Tempo CLI to remove affected traces from object storage first.
Redact traces removes traces containing sensitive data from object storage. To remove specific traces directly from the CLI, drop traces by ID.
Grafana tools for AI workflows
These capabilities aren’t specific to Tempo. You have them by working in the Grafana product family, and they apply across the platform. Their AI value is composability: an agent that starts in Tempo’s trace data can pivot outward to act on Grafana resources, chain investigations across other signals, and look up current documentation.
For additional information about AI capabilities in Grafana, refer to the AI and machine learning documentation.
gcx
gcx is a CLI for managing Grafana resources, including dashboards, data sources, alerting rules, and Grafana Cloud products like Synthetic Monitoring, SLO, and Adaptive Telemetry.
It works with Grafana Cloud, Grafana Enterprise, and Grafana OSS (v12 or later).
An agent can use gcx alongside the Tempo MCP server to act on what it finds in traces.
For example, after identifying a latency issue, the agent could query related Prometheus metrics or inspect alerting rules through gcx without leaving the terminal.
Grafana Assistant CLI
Grafana Assistant is an LLM-powered tool built into Grafana Cloud that queries data, builds dashboards, and helps you understand errors using natural language. It requires a Grafana instance where Grafana Assistant is enabled (for example, a Grafana Cloud stack).
The assistant-cli connects agents to Grafana Assistant through the Agent-to-Agent (A2A) API.
This lets an agent chain investigations across observability signals from the terminal.
For example, an agent could find a failing trace in Tempo, then use the assistant-cli to correlate the failure with error logs or query related metrics.
Documentation as Markdown
Grafana documentation is available as Markdown, so agents can fetch current reference material instead of relying on potentially outdated training data.
Next steps
- Set up the MCP server to give agents access to your tracing data.
- Install gcx to manage Grafana resources from the terminal or an agent workflow.
- Install the assistant-cli to connect agents to Grafana Assistant.
