Raygun MCP Server

The Raygun MCP server bridges the gap between AI assistants and your application’s crash‑reporting ecosystem. By exposing Raygun’s rich telemetry through the Model Context Protocol, developers can ask natural‑language questions about errors, deployments, and user impact without leaving their IDE or chat interface. This eliminates the need to manually sift through dashboards, export logs, or write custom queries—AI can pull the exact data you need on demand.

At its core, the server authenticates with Raygun via a Personal Access Token and then offers a suite of tools that mirror the most valuable features of the Raygun UI. Developers can investigate errors by retrieving stack traces, correlating them with specific releases, and identifying the affected users. The deployment tracking capability lets AI assistants pinpoint which release introduced a new issue, while the performance insights expose page load times and resource bottlenecks across time series. For teams, the server also supports user monitoring to surface session data and collaboration tools that manage invitations and track resolution status. Finally, the analytics layer provides histogram views and trend analysis, enabling quick visual checks of error frequency or latency spikes.

Real‑world scenarios that benefit from this integration include troubleshooting production incidents in real time, preparing release notes by automatically summarizing new crashes, and conducting root‑cause analyses during post‑mortems—all through a conversational interface. A developer can simply type, “Show me the top five errors from last week that affected more than 10 users” and receive a concise list, or ask for the impact of a specific deployment on latency. The server’s design ensures that these queries are fast, secure, and adhere to Raygun’s API limits.

Integrating the Raygun MCP server into existing AI workflows is straightforward: once registered, any MCP‑compatible assistant (Claude, Gemini, JetBrains AI, etc.) can invoke the server’s tools as if they were native extensions. The assistant’s prompt engine will handle intent parsing, send the appropriate API calls, and format the response for readability. Because the server exposes both raw data endpoints and higher‑level analytic tools, it can serve simple lookups as well as complex multi‑step investigations that involve correlating errors with user sessions or deployment timelines.

What sets this MCP server apart is its focus on contextual, conversational data retrieval. Rather than exposing raw API endpoints, it packages Raygun’s telemetry into semantic tools that an assistant can reason about. This reduces cognitive load for developers, speeds up debugging cycles, and enables non‑technical stakeholders to participate in error analysis through chat. By turning crash data into actionable conversation, the Raygun MCP server turns a traditionally siloed monitoring platform into an interactive AI‑powered partner.