Sentry MCP Server

Overview

The Sentry MCP Server is a lightweight, educational implementation of the Model Context Protocol that bridges AI assistants with the Sentry error‑tracking platform. By exposing a set of intuitive tools, it allows developers to query, analyze, and manage Sentry projects directly from conversational AI interfaces such as Claude, Cursor, or Codeium Windsurf. The server removes the need to manually interact with Sentry’s REST API, enabling rapid debugging and monitoring workflows within a single chat session.

This server solves the pain point of “context switching” when troubleshooting production issues. Instead of toggling between a terminal, the Sentry web UI, and an IDE, an AI assistant can retrieve project lists, resolve short issue identifiers, fetch detailed event data, and even create new projects—all through natural language commands. The result is a smoother developer experience where insights about crashes, performance bottlenecks, or release health are delivered instantly and in a format that can be embedded into code reviews or documentation.

Key capabilities include:

• Project discovery () that enumerates all accessible projects for a given organization, with optional formatting to suit plain text or markdown outputs.
• Issue resolution () that translates a concise issue code into full details, making it easy to reference errors in discussions.
• Event analysis () for pulling a specific event from an issue, enabling deep dives into stack traces or performance metrics without leaving the chat.
• Bulk event listing () to surface recent failures across a project, useful for trend analysis or triage.
• Project creation () which automates the onboarding of new services into Sentry, returning client keys for immediate instrumentation.
• Issue enumeration () to provide a quick snapshot of all active problems within a project.

In real‑world scenarios, this MCP server shines when developers need to triage incidents during a sprint, review error trends before a release, or onboard new microservices into monitoring. By integrating directly with AI workflows, it eliminates repetitive API calls and lets teams focus on solving bugs rather than fetching data. The server’s design prioritizes simplicity, making it an ideal teaching tool for newcomers to MCP while still offering practical functionality for seasoned developers.