Sentry Mcp

What is Sentry Mcp?

Sentry Mcp is a Model Context Protocol (MCP) server implemented in TypeScript that connects AI models to the Sentry error tracking service. It allows AI models to query and analyze error reports and events on Sentry.

How to use Sentry Mcp?

Install dependencies with npm install, build the project with npm run build, and run the server via standard I/O using node dist/index.js. Set the required environment variable SENTRY_AUTH_TOKEN (and optionally SENTRY_ORGANIZATION_SLUG, SENTRY_PROJECT_SLUG, SENTRY_BASE_URL) in a .env file or at runtime. Debug with the MCP Inspector using npx @modelcontextprotocol/inspector node dist/index.js.

Key features of Sentry Mcp

• get_sentry_issue tool retrieves and analyzes Sentry issues by ID or URL.
• sentry-issue prompt template formats issue details as conversation context.
• Returns issue title, ID, status, level, first/last seen timestamps, event count, and complete stack trace.
• Configuration via environment variables with fallback to runtime values.

Use cases of Sentry Mcp

• Automatically retrieve and explain a specific Sentry error during a debugging conversation.
• Provide structured issue details as context for an AI assistant to suggest fixes.
• Integrate Sentry error analysis into AI‑powered developer workflows.

FAQ from Sentry Mcp

What runtime dependencies does Sentry Mcp require?

Node.js and npm are required to install and run the server.

How is Sentry Mcp authenticated with Sentry?

It uses a Sentry authentication token set via the SENTRY_AUTH_TOKEN environment variable.

Where does the data retrieved by Sentry Mcp come from?

All data comes from the Sentry API; the server queries your Sentry project and organization.

What transport does Sentry Mcp use?

The server communicates via standard I/O (stdio) and can be debugged with the MCP Inspector over a subprocess.

Are there any known limits mentioned in the documentation?

No limits or rate‑limit details are provided in the README.