Exposes Go pprof profiling data to AI coding assistants, facilitating deep performance analysis and optimization.
Sponsored
mcp-pprof serves as a Model Context Protocol (MCP) server, wrapping `go tool pprof` to make Go performance profiling data accessible and analyzable by AI coding assistants. It empowers AI agents to parse various pprof files (CPU, heap, block, mutex, goroutine profiles), generate SVG flamegraphs, pinpoint performance bottlenecks and hotspots, compare different profile files, and offer actionable optimization suggestions, seamlessly integrating performance insights into AI-driven development workflows.
Key Features
01Provides comprehensive tools to parse, analyze, and compare Go pprof files
02Offers deep performance analysis capabilities with actionable optimization suggestions
03Generates SVG flamegraphs for visual identification of performance bottlenecks
04Supports multiple transport modes including stdio (local) and HTTP (mcp-remote)
050 GitHub stars
06Fully compatible with the Model Context Protocol (MCP) for seamless AI integration
Use Cases
01Analyze CPU and heap profiles to identify performance bottlenecks and hot functions in Go applications
02Generate visual flamegraphs to quickly understand call stack and resource consumption patterns
03Compare 'before' and 'after' profiles to evaluate performance improvements or regressions
