Trunktail, developed by Elliothux, is an MCP server that connects Trunk Tail observability to AI coding assistants for in-session debugging. The tool lets AI assistants access, search, and analyze live logs and distributed traces from Trunk.io so assistants can provide context-aware troubleshooting inside IDEs and chat interfaces. It integrates with MCP clients, supports advanced log search, real-time retrieval, and environment-variable API authentication. Target users are software engineers and DevOps using Trunk.io who need quicker issue diagnosis within their development workflow.

What tasks can you actually use it for?

The tool connects AI assistants to Trunk Tail logs and distributed traces so users can ask models about specific events, errors, or traces during development. It supports targeted queries for events and error searches, enabling an assistant to surface relevant telemetry without manual copy-paste. Typical use cases include: Those scenarios help surface relevant log snippets for an assistant to reference.

• in-IDE troubleshooting
• triage of failing tests
• investigation of recent production errors

How reliable are the tool's outputs for debugging?

The tool supplies the assistant with up-to-date telemetry because it performs real-time data retrieval from Trunk.io, which supports current system state inspections. Fidelity depends on the observability data available in Trunk Tail; queries return whatever logs and traces exist. For high-stakes decisions, outputs require human verification against original logs and traces since the model's responses reflect retrieved telemetry rather than guaranteed root-cause resolution.

What input and setup does it require?

Requires an MCP-compatible client such as Claude Desktop or Cursor and an active Trunk.io account with API access. Typical deployment is a Node.js server that reads Trunk API credentials from environment variables for authentication. Configuration involves adding the server entry to the MCP client configuration so the assistant can query the proxy. Lack of Trunk API access prevents the tool from retrieving telemetry.

How does it fit into workflows and handle data privacy?

The tool runs as a local proxy so telemetry requests remain under the MCP client's control and align with team security practices. API keys and data handling follow the MCP client and Trunk.io privacy policies because the server relays queries rather than embedding them in external dashboards. Its open-source codebase allows teams to inspect the proxy behavior and contribute fixes or audit traces before adoption.

A practical choice for Trunk.io teams that require human validation

The tool is a practical option for engineering teams who want AI-assisted examination of runtime telemetry during development. Its utility depends on available Trunk.io telemetry and on reviewers confirming model suggestions against source logs. For safer adoption, treat assistant findings as hypotheses and validate them manually before changing code or configuration; teams that enforce review of outputs will extract the most value.