Provides access to Scout APM performance monitoring data for Django applications, including traces, errors, endpoint metrics, and performance insights
Provides access to Scout APM performance monitoring data for FastAPI applications, including traces, errors, endpoint metrics, and performance insights
Can be combined with AI assistants to create GitHub issues based on Scout APM error and performance data
Can be combined with AI assistants to create GitLab issues based on Scout APM error and performance data
Can be combined with AI assistants to create Jira tickets with detailed error and performance information from Scout APM
Provides access to Scout APM performance monitoring data for Laravel applications, including traces, errors, endpoint metrics, and performance insights
Provides access to Scout APM performance monitoring data for PHP applications, including traces, errors, endpoint metrics, and performance insights
Provides access to Scout APM performance monitoring data for Python applications, including traces, errors, endpoint metrics, and performance insights
Provides access to Scout APM performance monitoring data for Ruby applications, including traces, errors, endpoint metrics, and performance insights
Scout Monitoring MCP
This repository contains code to locally run an MCP server that can access Scout Monitoring data via Scout's API. We provide a Docker image that can be pulled and run by your AI Assistant to access Scout Monitoring data.
This puts Scout Monitoring's performance and error data directly in the hands of your AI Assistant. For Rails, Django, FastAPI, Laravel and more. Use it to get traces and errors with line-of-code information that the AI can use to target fixes right in your editor and codebase. N+1 queries, slow endpoints, slow queries, memory bloat, throughput issues - all your favorite performance problems surfaced and explained right where you are working.
If this makes your life a tiny bit better, why not :star: it?!
Prerequisites
You will need to have or create a Scout Monitoring account and obtain an API key.
Install the Scout Agent in your application and send Scout data!
Visit settings to get or create an API key
This is not your "Agent Key"; it's the "API Key" that can be created on the Settings page
This is a read-only key that can only access data in your account
Install Docker. Instructions below assume you can start a Docker container
The MCP server will not currently start without an API key set, either in the environment or by a command-line argument on startup.
Installation
We recommend using the provided Docker image to run the MCP server. It is intended to be started by your AI Assistant and configured with your Scout API key. Many local clients allow specifying a command to run the MCP server in some location. A few examples are provided below.
The Docker image is available on Docker Hub.
Of course, you can always clone this repo and run the MCP server directly; uv
or other
environment management tools are recommended.
Setup Wizard
The simplest way to configure and start using the Scout MCP is with our interactive setup wizard:
Run via npx:
Build and run from source:
The wizard will guide you through:
Selecting your AI coding platform (Cursor, Claude Code, Claude Desktop)
Entering your Scout API key
Automatically configuring the MCP server settings
Supported Platforms
The wizard currently supports setup for:
Cursor - Automatically configures MCP settings
Claude Code (CLI) - Provides the correct command to run
Claude Desktop - Updates the configuration file for Windows/Mac
Configure a local Client (e.g. Claude/Cursor/VS Code Copilot)
If you would like to configure the MCP manually, this usually just means supplying a command to run the MCP server with your API key in the environment to your AI Assistant's config. Here is the shape of the JSON (the top-level key varies):
MAKE SURE to update the SCOUT_API_KEY
value to your actual api key in
Arguments
in the Cursor Settings > MCP
We recommend the "Add an MCP server to your workspace" option
Add the following to your claude config file:
macOS:
~/Library/Application Support/Claude/claude_desktop_config.json
Windows:
%APPDATA%/Claude/claude_desktop_config.json
Token Usage
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 Code export MAX_MCP_OUTPUT_TOKENS=50000
), we recommend
setting it generously high, e.g. 50,000 tokens.
Usage
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
Tool
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 dateget_app_metrics
- Get individual metric data (response_time, throughput, etc.) for a specific applicationget_app_endpoints
- Get all endpoints for an application with aggregated performance metricsget_endpoint_metrics
- Get timeseries metrics for a specific endpoint in an applicationget_app_endpoint_traces
- Get recent traces for an app filtered to a specific endpointget_app_trace
- Get an individual trace with all spans and detailed execution informationget_app_error_groups
- Get recent error groups for an app, optionally filtered by endpointget_app_insights
- Get performance insights including N+1 queries, memory bloat, and slow queries
Useful Prompts
"Summarize the available tools in the Scout Monitoring MCP."
"Find the slowest endpoints for app
my-app-name
in 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 app
Foo
in the last 24 hours. Get the latest error detail, examine the backtrace and suggest a fix.""Get any recent n+1 insights for app
Bar
. Pull the specific trace by id and help me optimize it based on the backtrace data."
Local Development
We use uv
and taskipy
to manage environments and run tasks for this project.
Run with Inspector
Connect within inspector to add API key, set to STDIO transport
Build the Docker image
local-only server
The server can only run on the client's local machine because it depends on local resources.
Tools
Enables AI assistants to access Scout Monitoring performance and error data through Scout's API. Provides traces, errors, metrics, and insights for Rails, Django, FastAPI, Laravel and other applications to help identify and fix performance issues like N+1 queries, slow endpoints, and memory bloat.