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
01List available Scout APM applications and their status.
02Retrieve individual metric data (response time, throughput) for specific applications.
03Access detailed performance metrics and recent traces for application endpoints.
04Obtain full individual traces with span and detailed execution information.
05Identify performance insights including N+1 queries, memory bloat, and slow queries, and recent error groups.
0618 GitHub stars
Use Cases
01Generate comprehensive GitHub/GitLab issues based on detailed error and performance data.
02Create rich JIRA tickets automatically populated with performance problems and error insights.
03Generate Pull Requests that directly address and fix specific errors or performance bottlenecks identified by AI.
