MCP Sentry Server

MCP Sentry Server Overview

The MCP Sentry server is a dedicated bridge between AI assistants and the Sentry error‑tracking platform. By exposing Sentry’s rich API through the Model Context Protocol, it allows language models to query, analyze, and react to real‑world application errors without leaving the MCP ecosystem. Developers can therefore build intelligent debugging assistants that surface critical issues, pinpoint root causes, and even trigger remediation workflows—all from within a single LLM session.

At its core, the server implements two communication channels. The first is the standard MCP stream over stdin/stdout, which is ideal for local or containerized deployments where a lightweight, process‑level connection suffices. The second is a Server‑Sent Events (SSE) endpoint that exposes the same functionality over HTTP, enabling web‑based agents or browser extensions to subscribe to real‑time error streams. This duality gives teams flexibility: use the fast, low‑overhead stream for internal tooling or the SSE interface when integrating with dashboards and notification systems.

Key capabilities include a set of reusable prompts such as and , which let a model retrieve a single issue by ID or find the most impactful problem from an issues list URL. Complementing these are tools— and —that return structured data objects containing title, status, severity, timestamps, event counts, and full stack traces. The server also exposes a comprehensive API for listing available prompts and tools, making it straightforward to discover functionality programmatically.

Real‑world use cases abound. A QA engineer could ask the assistant, “What is the latest critical crash affecting users?” and receive a concise summary plus stack trace. A support engineer might trigger to surface the problem that is hurting the largest user base, then automatically open a Jira ticket via an integrated tool. In continuous integration pipelines, the server can be invoked to validate that no new high‑severity issues appear before a release is merged.

Integrating MCP Sentry into an AI workflow is simple: configure the server’s section with the appropriate command and environment variables, then invoke the desired prompt or tool from your LLM client. The server handles authentication, pagination, and error mapping internally, returning clean, typed responses that the model can consume or present to end users. This tight coupling of error data with conversational AI removes manual lookup steps, reduces context switching, and accelerates incident response across teams.