Advanced Python Function Debugging with MCP Integration

Gnosis Mystic 🔮 AI-Powered Python Function Analysis and Control Gnosis Mystic gives AI assistants direct access to your Python functions through runtime hijacking and intelligent analysis. Add minimal decorators, and Claude can inspect, optimize, and control your code in real-time. Inspiration and Work Mystic was inspired by Giantswarm's mcp-debug. Code by fairly stock Claude Code. Prompts, code sketches, and planning by Claude Desktop using Gnosis Evolve tools. ✨ Why Gnosis Mystic? The Problem AI assistants are blind to your running code: They can't see function performance in real-time No direct access to runtime behavior and state Can't dynamically test optimizations Limited to static code analysis No way to experiment with function modifications safely The Solution Gnosis Mystic creates a direct AI-to-code interface: AI sees everything : Real-time function calls, performance, and behavior : Real-time function calls, performance, and behavior Safe experimentation : Test caching, mocking, and optimizations instantly : Test caching, mocking, and optimizations instantly Runtime control : AI can modify function behavior without code changes : AI can modify function behavior without code changes Intelligent analysis : AI discovers bottlenecks and suggests improvements : AI discovers bottlenecks and suggests improvements Live debugging: AI can inspect function state during execution 🚀 Core Capabilities 1. AI-Visible Function Monitoring @ hijack_function ( AnalysisStrategy ()) def fetch_user_data ( user_id ): response = requests . get ( f"https://api.example.com/users/ { user_id } " ) return response . json () # Claude can now see: # - Call frequency and patterns # - Performance metrics # - Parameter distributions # - Error rates and types 2. AI-Controlled Optimization # You add minimal decoration @ hijack_function () def expensive_calculation ( data ): # Your logic unchanged return complex_math ( data ) # Claude can experiment with: # - Adding caching strategies # - Performance profiling # - Mock data for testing # - Alternative implementations 3. Intelligent Security Analysis @ hijack_function ( SecurityStrategy ()) def process_payment ( user_id , credit_card , amount ): # Your business logic unchanged return payment_processor . charge ( credit_card , amount ) # Claude automatically detects and reports: # - Sensitive data in logs # - Security vulnerabilities # - Data flow patterns 4. Dynamic Behavior Control Runtime Strategies : AI can apply caching, mocking, blocking without restarts : AI can apply caching, mocking, blocking without restarts A/B Testing : Compare function implementations in real-time : Compare function implementations in real-time Environment Adaptation : Different behaviors for dev/test/prod : Different behaviors for dev/test/prod Performance Experiments: Test optimizations safely 🔧 Quick Start # Install from source git clone https://github.com/gnosis/gnosis-mystic.git cd gnosis-mystic pip install -e " .[web] " # Initialize your project cd /path/to/your/project mystic init # Start the server for AI integration mystic serve # Let Claude discover your functions mystic discover 🎯 Example Usage Basic AI Integration import mystic # Minimal decoration for AI visibility @ mystic . hijack () def api_call ( endpoint , data ): return requests . post ( f"https://api.com/ { endpoint } " , json = data ) # Claude can now: # - See all calls and responses # - Measure performance # - Suggest optimizations # - Test improvements Advanced Analysis @ mystic . hijack ( strategies = [ mystic . AnalysisStrategy ( track_performance = True ), mystic . SecurityStrategy ( scan_sensitive_data = True ) ] ) def process_user_data ( user_info ): # Your code unchanged return database . save ( user_info ) 💡 Real-World AI Integration Claude Desktop Setup Initialize your project: cd /your/project mystic init Start the server: mystic serve Add to Claude Desktop config: { "mcpServers" : { "gnosis-mystic" : { "command" : " python " , "args" : [ " C: \\ path \\ to \\ gnosis-mystic \\ mystic_mcp_standalone.py " , " --project-root " , " C: \\ your \\ project " ] } } } AI-Powered Development: "Find my slowest functions" - Claude analyzes performance data - Claude analyzes performance data "Add caching to expensive calls" - Claude applies optimizations - Claude applies optimizations "Check for security issues" - Claude scans for vulnerabilities - Claude scans for vulnerabilities "Show me error patterns" - Claude analyzes failure modes - Claude analyzes failure modes "Optimize this function" - Claude experiments with improvements 🧠 AI Assistant Capabilities Once integrated, Claude can: Function Discovery & Analysis Automatically find all decorated functions Analyze call patterns and performance Identify bottlenecks and optimization opportunities Generate performance reports Real-Time Optimization Apply caching strategies dynamically Test different implementations A/B test performance improvements Rollback changes instantly Security & Debugging Detect sensitive data exposure Track function call flows Identify error patterns Debug production issues safely Code Intelligence Suggest function improvements Recommend architectural changes Predict performance impacts Generate optimization plans 📊 Current Status ✅ What's Working Now Function Hijacking : Runtime interception with multiple strategies : Runtime interception with multiple strategies AI Integration : Claude can discover and control functions via MCP : Claude can discover and control functions via MCP Performance Tracking : Real-time metrics with minimal overhead : Real-time metrics with minimal overhead Security Analysis : Automatic sensitive data detection : Automatic sensitive data detection CLI Tools: Function discovery and server management 🚧 Coming Soon Enhanced AI analysis capabilities Web dashboard for monitoring IDE extensions for VS Code/Cursor Distributed debugging support 🏗️ How It Works Gnosis Mystic creates a bridge between your code and AI: Minimal Decoration: Add simple decorators to functions you want monitored Runtime Interception: Captures all function calls and behavior AI Communication: Streams data to AI assistants via MCP protocol Dynamic Control: AI can modify function behavior in real-time Safe Experimentation: Test changes without affecting core logic Your Function + @hijack_function → Mystic Layer → AI Analysis ↑ ↓ └────── Core Logic Preserved ←──── AI Control ──┘ 🎯 Use Cases Development & Debugging Performance Profiling : AI identifies slow functions automatically : AI identifies slow functions automatically Error Analysis : AI patterns in failures and suggests fixes : AI patterns in failures and suggests fixes Code Quality: AI reviews function behavior and suggests improvements Production Monitoring Real-time Optimization : AI applies performance improvements live : AI applies performance improvements live Security Monitoring : AI detects suspicious patterns or data leaks : AI detects suspicious patterns or data leaks Capacity Planning: AI predicts scaling needs from usage patterns Testing & QA Intelligent Mocking : AI creates realistic test data : AI creates realistic test data Behavior Verification : AI ensures functions work as expected : AI ensures functions work as expected Regression Detection: AI spots when function behavior changes 🤝 Contributing We welcome contributions! See CONTRIBUTING.md for guidelines. 📄 License Apache 2.0 License - see LICENSE for details. 🔗 Related Projects gnosis-evolve : Original function hijacking foundation : Original function hijacking foundation mcp-debug : MCP debugging reference implementation (inspiration) : MCP debugging reference implementation (inspiration) Claude Desktop: Primary AI assistant integration target The future of Python development: Your code, enhanced by AI. 🔮✨ Imagine Claude knowing exactly how your functions behave, optimizing them in real-time, and debugging issues before you even notice them. That's Gnosis Mystic.