Announcing Scout’s MCP Server for AI-Native Monitoring!

We’re excited to introduce the Scout Monitoring MCP Server  — a new way to bring AI-native monitoring directly into your coding assistant. Instead of flipping between dashboards and logs, the MCP (Model Context Protocol) server surfaces performance data, errors, and slow endpoints right where you work. Ask plain-language questions like “show me the latest five errors” and get answers grounded in live telemetry. You can even let your coding assistant propose and push fixes! 

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In short: Scout MCP Server keeps you in flow, kills context-switching, and makes AI-powered development faster and way less painful.

AI-Native Monitoring and Why It Matters

Curious what AI-Native Monitoring actually means? We’ve got a post breaking it down. But for now, here’s how Scout is changing the game: 

• Turn your LLM into a monitoring hub
  Forget opening another dashboard just to see what’s wrong. With Scout MCP, your coding assistant (Claude Code, etc.) can pull in performance data, error traces, and slow endpoints directly inside your LLM. Everything you need shows up where you already work — no extra tabs, no context-switching.

• Scout MCP Server means tighter detection, diagnosis, and resolution
  Imagine this: your assistant tells you “there’s a spike in latency on endpoint /api/search”, then “here’s a proposed fix”, and it even pushes the PR for you. No more “I’ll get to it tomorrow” delays.

• Unified tools, less friction
  Tired of bouncing between APM, error trackers, and log dashboards? With MCP, it’s all in one place. Metrics, errors, and performance data come through a single, consistent interface. This is monitoring that’s built with AI-driven workflows in mind – not patched onto old ones.

How to Get Started with Scout MCP Server

1. Sign up / log in to Scout Monitoring
2. Install the Scout Agent in your application and send Scout data:
   
   1. Ruby
   2. Python
   3. PHP
   4. If trying this out locally, for the best experience, make sure that monitor: true and errors_enabled: true are set in your config
3. Visit settings to get or create an API key.
4. Install Docker.
5. Check out our GitHub repo — you’ll be up and running in minutes.

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Thanks for taking a look. We are excited to hear what you think!

Cheers,
The Scout Team

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