Scout Monitoring MCP

What can you do with Scout Monitoring MCP?

- List your Scout applications— Ask the assistant to retrieve all monitored apps withlist_apps, optionally filtering by recent activity.
- Identify slow endpoints— Useget_app_endpointsandget_endpoint_metricsto surface endpoints with high response times or throughput issues.
- Inspect individual traces— Pull a specific trace viaget_app_traceto examine span-level execution details and backtrace data.
- Review recent error groups— Fetch error groups withget_app_error_groupsand examine backtraces to suggest targeted code fixes.
- Surface performance insights— Retrieve N+1 query detections, memory bloat, and slow query warnings usingget_app_insights.

This repository contains code to locally run an MCP server that can accessScout Monitoringdata via Scout's API. We provide a Docker image that can be pulled and run by your AI Assistant to access Scout Monitoring data.

cd ./wizard npm install npm run build node dist/wizard.js

The Wizard is a great way to get started, but you can also set things up manually. You will need to have or create a Scout Monitoring account and obtain an API key.
- Sign up
- Install the Scout Agent in your application and send Scout data!

- Ruby
- Python
- PHP
- If you are trying this out locally, make suremonitor: true,errors_enabled: trueare set in your config for the best experience

The Docker image is available onDocker Hub.

{ "mcpServers": { "scout-apm": { "command": "docker", "args": ["run", "--rm", "-i", "--env", "SCOUT_API_KEY", "scoutapp/scout-mcp-local"], "env": { "SCOUT_API_KEY": "your_scout_api_key_here"} } } }

claude mcp add scoutmcp -e SCOUT_API_KEY=your_scout_api_key_here -- docker run --rm -i -e SCOUT_API_KEY scoutapp/scout-mcp-local

MAKE SURE to update theSCOUT_API_KEYvalue to your actual api key inArgumentsin the Cursor Settings > MCP

- VS Code Copilot docs

{ "mcpServers": { "scout-apm": { "command": "docker", "args": ["run", "--rm", "-i", "--env", "SCOUT_API_KEY", "scoutapp/scout-mcp-local"], "env": { "SCOUT_API_KEY": "your_scout_api_key_here"} } } }

Scout's MCP is intended to put error and performance data directly in the... hands? of your AI Assistant. Use it to get traces and errors with line-of-code information that the AI can use to target fixes right in your editor.

Most assistants will show you both raw tool calls and perform analysis. Desktop assistants can readily create custom JS applications to explore whatever data you desire. Assistants integrated into code editors can use trace data and error backtraces to make fixes right in your codebase.

Combine Scout's MCP with your AI Assistant's other tools to:

- Create rich GitHub/GitLab issues based on errors and performance data
- Make JIRA fun - have your AI Assistant create tickets with all the details
- Generate PRs that fix specific errors and performance problems

The Scout MCP provides the following tools for accessing Scout APM data:

- list_apps- List available Scout APM applications, with optional filtering by last active date
- get_app_metrics- Get individual metric data (response_time, throughput, etc.) for a specific application
- get_app_endpoints- Get all endpoints for an application with aggregated performance metrics
- get_endpoint_metrics- Get timeseries metrics for a specific endpoint in an application
- get_app_endpoint_traces- Get recent traces for an app filtered to a specific endpoint
- get_app_trace- Get an individual trace with all spans and detailed execution information
- get_app_error_groups- Get recent error groups for an app, optionally filtered by endpoint
- get_app_insights- Get performance insights including N+1 queries, memory bloat, and slow queries

The Scout MCP provides configuration templates as resources that your AI assistant can read and apply:

- scoutapm://config-resources/{framework}- Setup instructions for supported framework or library (rails, django, flask, fastapi)
- scoutapm://config-resources/list- List all available configuration templates
- scoutapm://metrics- List of all available metrics for Scout APM

- "Help me set up Scout monitoring for my Rails application"
- "Create a Scout APM config file for my Django project with key ABC123"

- "Summarize the available tools in the Scout Monitoring MCP."
- "Find the slowest endpoints for appmy-app-namein the last 7 days. Generate a table with the results including the average response time, throughput, and P95 response time."
- "Show me the highest-frequency errors for appFooin the last 24 hours. Get the latest error detail, examine the backtrace and suggest a fix."
- "Get any recent n+1 insights for appBar. Pull the specific trace by id and help me optimize it based on the backtrace data."

We are currently more interested in expanding available information than strictly controlling response size from our MCP tools. If your AI Assistant has a configurable token limit (e.g. Claude Codeexport MAX_MCP_OUTPUT_TOKENS=50000), we recommend setting it generously high, e.g. 50,000 tokens.

We useuvandtaskipyto manage environments and run tasks for this project.

Connect within inspector to add API key, set to STDIO transport
- Branch and bump versions withuv run python bump_versions.py
- Get that merged
- Create a GitHub release with the new version (gh release create v2025.11.3 --generate-notes --draft)

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