pprof-mcp-agent

# pprof-mcp-agent A Go agent that provides runtime profiling data through the Model Context Protocol (MCP). It enables real-time collection and analysis of Go runtime performance data, making it easier to monitor and debug Go applications in production. ## Installation ```bash go get github.com/yudppp/pprof-mcp-agent ``` ## Overview pprof-mcp-agent integrates Go's built-in profiling capabilities with the MCP protocol, providing a streamlined way to collect and analyze performance data. It supports various profile types, each designed to help diagnose different aspects of your application's performance: ### Supported Profile Types - **CPU Profile**: Identifies CPU-intensive code paths and performance bottlenecks - Configurable sampling duration (default: 10 seconds) - Shows where your program spends its execution time - Useful for optimizing CPU-bound applications - **Heap Profile**: Analyzes memory usage patterns - Shows current memory allocations by location - Helps identify memory leaks and inefficient memory usage - Includes both in-use and allocated memory statistics - **Goroutine Profile**: Examines goroutine behavior - Shows currently running goroutines and their states - Helps identify goroutine leaks and deadlocks - Provides detailed stack traces for debugging - **Block Profile**: Analyzes synchronization issues - Shows where goroutines block on synchronization primitives - Helps identify contention points and performance bottlenecks - Includes blocking duration statistics - **Allocation Profile**: Examines memory allocation patterns - Shows memory allocation frequency by location - Helps identify memory churn and optimization opportunities - Includes both allocated and freed memory statistics - **Thread Creation Profile**: Monitors OS thread creation - Shows where new OS threads are created - Helps identify thread leaks and excessive thread creation - Useful for analyzing thread pool behavior ## Profile View Modes Each profile can be viewed in three different modes: - **Flat View** (default): Shows direct values for each function - Displays the time/memory/etc. spent directly in each function - Excludes time spent in functions called by this function - Best for identifying specific hot spots in the code - **Cumulative View**: Shows cumulative values including child functions - Includes time spent in the function and all functions it calls - Helps identify high-level bottlenecks in the call hierarchy - Useful for understanding the full impact of function calls - **Graph View**: Shows the call graph relationship - Displays parent-child relationships between functions - Shows the top 5 children for each function - Helps understand the call flow and identify problematic paths ## Usage ### Basic Integration ```go package main import ( "context" "log" pprofmcpagent "github.com/yudppp/pprof-mcp-agent" ) func main() { // Create a context for controlling the server lifecycle ctx := context.Background() // Start the MCP server on port 1239 if err := pprofmcpagent.ServeSSE(ctx, ":1239"); err != nil { log.Fatalf("Failed to start pprof MCP agent: %v", err) } } ``` ### Configuration Each profile type supports the following configuration options: - `limit`: Maximum number of locations to show in results (default: 100, min: 100, max: 10000) - `view`: Profile view mode (`flat`, `cum`, or `graph`, default: `flat`) - `duration`: Sampling duration for CPU profiles (default: 10 seconds) ## Features - **Real-time Profiling**: Collect profiling data from running applications - **Multiple View Modes**: Analyze data in flat, cumulative, or graph views - **Aggregated Results**: View aggregated statistics for better analysis - **Multiple Profile Types**: Comprehensive coverage of different performance aspects - **Easy Integration**: Simple API for quick integration into existing applications - **MCP Protocol**: Standard protocol for reliable data delivery - **Configurable**: Adjustable parameters for different profiling needs ## Contributing Contributions are welcome! Here's how you can help: 1. Fork the repository 2. Create your feature branch (`git checkout -b feature/amazing-feature`) 3. Commit your changes (`git commit -m 'Add some amazing feature'`) 4. Push to the branch (`git push origin feature/amazing-feature`) 5. Open a Pull Request Please ensure your PR includes appropriate tests and documentation updates. ## License [The MIT License (MIT)](https://github.com/yudppp/pprof-mcp-agent/blob/main/LICENSE) ## Author yudppp ## Support If you encounter any issues or have questions, please file an issue on the GitHub repository.