Scout Monitoring
Provide AI Assistants with real-time application performance and error data for targeted code analysis and fixes.
About
Scout Monitoring's local MCP server empowers AI Assistants by integrating application performance and error data directly into their workflows. It allows AI models to access traces, errors with line-of-code information, and performance insights like N+1 queries, slow endpoints, and memory bloat from various frameworks including Rails, Django, and FastAPI. This direct data access enables AI Assistants to identify and suggest fixes for performance problems and errors right within your editor and codebase, significantly enhancing development efficiency and reducing debugging time.
Key Features
- List available Scout APM applications and their status.
- Retrieve individual metric data (response time, throughput) for specific applications.
- Access detailed performance metrics and recent traces for application endpoints.
- Obtain full individual traces with span and detailed execution information.
- Identify performance insights including N+1 queries, memory bloat, and slow queries, and recent error groups.
- 18 GitHub stars
Use Cases
- Generate comprehensive GitHub/GitLab issues based on detailed error and performance data.
- Create rich JIRA tickets automatically populated with performance problems and error insights.
- Generate Pull Requests that directly address and fix specific errors or performance bottlenecks identified by AI.
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