Raygun MCP Server

Overview

The Raygun MCP Server bridges the gap between AI assistants and Raygun’s rich crash‑reporting and real‑user monitoring ecosystem. By exposing Raygun API V3 endpoints through the Model Context Protocol, it allows developers to query, update, and manage application health directly from conversational agents. This eliminates the need for manual API calls or custom scripts, streamlining incident response and performance analysis within a single AI‑driven workflow.

At its core, the server offers a comprehensive suite of tools that mirror Raygun’s native capabilities. From listing applications and retrieving detailed error groups to managing deployments, sessions, and source maps, each tool is mapped to a clear, purpose‑driven operation. Developers can quickly resolve or activate error groups, reprocess deployment commits, and upload source maps—all without leaving the chat interface. This level of integration is particularly valuable for teams that rely on continuous monitoring; AI assistants can surface trending issues, automatically adjust error statuses, or even trigger re‑deployments in response to detected anomalies.

Key features include:

• Full API coverage: Every major Raygun endpoint is represented, from application discovery to team invitations.
• Granular control: Tools like , , and give precise status management.
• Deployment lifecycle support: Create, update, delete, or reprocess deployments to keep error attribution accurate.
• Performance insights: Retrieve time‑series and histogram metrics for pages, enabling AI to generate actionable reports.
• Source map management: Upload, update, or delete source maps directly through the protocol, simplifying debugging workflows.

Typical use cases include automated incident triage, where an AI assistant scans for newly resolved error groups and notifies the engineering team; or real‑time performance monitoring, where conversational queries return latency trends for specific pages. In a DevOps pipeline, the server can be invoked to fetch deployment details or adjust error group statuses as part of a CI/CD notification system.

Integration with AI workflows is seamless: the MCP server operates over standard input/output, so any Claude‑compatible client can register it as a tool. Once configured, developers can invoke commands like or within natural language prompts, receiving structured JSON responses that can be parsed or visualized by downstream tooling. The result is a powerful, context‑aware assistant that turns raw telemetry into actionable insights without leaving the chat interface.