Sentry MCP Server

Overview

The Mcp Sentry server bridges the gap between an AI assistant and a production‑grade error monitoring platform. By exposing Sentry.io’s issue data through the Model Context Protocol, developers can query real‑time error reports, stack traces, and event statistics directly from the conversational context of Claude or other MCP‑compliant assistants. This eliminates the need to switch tools or manually export data, allowing debugging workflows to stay within a single integrated environment.

At its core, the server offers two primary tools:

• retrieves a specific issue by its ID or URL, returning detailed metadata such as title, status, severity level, timestamps, event count, and the full stack trace.
• fetches a list of issues for a given project and organization, providing concise summaries that include the same key fields. These tools are designed to be lightweight yet comprehensive, enabling an assistant to surface the most relevant information without overwhelming the user.

A complementary prompt——formats the retrieved data into a conversationally friendly layout. When invoked, it presents the issue details as natural language context that can be immediately referenced or expanded upon by subsequent tool calls. This two‑step approach (tool for data retrieval, prompt for formatting) gives developers fine‑grained control over how information is displayed and reused.

In practice, Mcp Sentry empowers several real‑world scenarios: a developer asking the assistant to “show me the latest critical error in project X” receives an instant, fully parsed stack trace; a QA engineer can ask for the list of unresolved issues before a release; or a support engineer can pull up an issue’s history during a ticket conversation. Because the server works with standard Sentry authentication tokens and project identifiers, it integrates seamlessly into existing CI/CD pipelines or local development workflows without exposing credentials in code.

What sets Mcp Sentry apart is its minimal friction integration. The server can be launched via a single command line, Docker container, or as part of the Smithery ecosystem. Once configured in a client’s settings file, any MCP‑enabled assistant can issue the two tools or prompt with no additional setup. This turnkey experience makes it an ideal choice for teams that already rely on Sentry for error tracking but want to augment their debugging process with conversational AI, all while keeping data flow secure and contextually relevant.