Advanced Python Function Debugging with MCP Integration

5 hours ago 1

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.

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.

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

Gnosis Mystic creates a direct AI-to-code interface:

AI sees everything: Real-time function calls, performance, and behavior
Safe experimentation: Test caching, mocking, and optimizations instantly
Runtime control: AI can modify function behavior without code changes
Intelligent analysis: AI discovers bottlenecks and suggests improvements
Live debugging: AI can inspect function state during execution

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
A/B Testing: Compare function implementations in real-time
Environment Adaptation: Different behaviors for dev/test/prod
Performance Experiments: Test optimizations safely

# 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

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

@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

Initialize your project:

cd /your/project mystic init
Start the server:
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
- "Add caching to expensive calls" - Claude applies optimizations
- "Check for security issues" - Claude scans for vulnerabilities
- "Show me error patterns" - 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

Apply caching strategies dynamically
Test different implementations
A/B test performance improvements
Rollback changes instantly

Detect sensitive data exposure
Track function call flows
Identify error patterns
Debug production issues safely

Suggest function improvements
Recommend architectural changes
Predict performance impacts
Generate optimization plans

Function Hijacking: Runtime interception with multiple strategies
AI Integration: Claude can discover and control functions via MCP
Performance Tracking: Real-time metrics with minimal overhead
Security Analysis: Automatic sensitive data detection
CLI Tools: Function discovery and server management

Enhanced AI analysis capabilities
Web dashboard for monitoring
IDE extensions for VS Code/Cursor
Distributed debugging support

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 ──┘

Performance Profiling: AI identifies slow functions automatically
Error Analysis: AI patterns in failures and suggests fixes
Code Quality: AI reviews function behavior and suggests improvements

Real-time Optimization: AI applies performance improvements live
Security Monitoring: AI detects suspicious patterns or data leaks
Capacity Planning: AI predicts scaling needs from usage patterns

Intelligent Mocking: AI creates realistic test data
Behavior Verification: AI ensures functions work as expected
Regression Detection: AI spots when function behavior changes

We welcome contributions! See CONTRIBUTING.md for guidelines.

Apache 2.0 License - see LICENSE for details.

gnosis-evolve: Original function hijacking foundation
mcp-debug: 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.

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