A comprehensive framework for developing AI systems that automatically utilize MCP (Model Context Protocol) tools while maintaining authenticity and preventing behavioral drift.
This framework emerged from a practical experiment: "How do we get AI systems to actually use the tools available to them?" Instead of waiting for explicit instructions, this system creates proactive AI behavior that automatically leverages MCP servers while maintaining safety and authenticity.
- Tool Adoption Gap: AIs often ignore available MCP tools unless explicitly prompted
- Performative Behavior: AI systems that claim capabilities without actual verification
- Behavioral Drift: Gradual degradation from helpful to sycophantic or potentially harmful
- Learning Without Safety: How to improve AI behavior without compromising core directives
- Token Burn: Excessive token usage from redundant memory queries and tool testing
The core behavioral directive that creates automatic MCP tool usage:
- Initialization Phase: Load authenticity framework + test tool availability
- Execution Phase: Proactive tool utilization + authenticity validation
- Validation Phase: Pre-response checks + continuous learning
Safe learning architecture that prevents directive drift:
- Prime Directive (immutable): Core behavioral rules
- Secondary Rules (proven): Validated behavioral improvements
- Tertiary Rules (contextual): User-validated adaptations
- Quaternary Rules (experimental): Require explicit approval
Prevents performative behavior through:
- Self-Audit Questions: "Have I verified this claim?"
- External Verification: Grounding in actual tests/evidence
- Observable Metrics: Tracking prediction accuracy and decision quality
- Safety Boundaries: Hard limits on experimental changes
Human-in-the-loop safeguards:
- Experimental Rule Safety Protocol: Prevents automatic override
- Auto-Correction Protocol: Immediate violation recovery
- User Correction Integration: Learning from human feedback
ai-mcp-framework/
├── README.md # This file
├── docs/
│ ├── architecture.md # System architecture overview
│ ├── implementation-guide.md # Step-by-step setup
│ └── research-background.md # Supporting research
├── framework/
│ ├── prime-directive-v2.txt # Enhanced MCP Prime Directive
│ ├── smart-memory-directive.txt # Token-optimized version (v2.2)
│ ├── authenticity-controls.json # Self-audit questions & metrics
│ ├── hierarchical-learning.json # Learning governance rules
│ ├── safety-protocols.json # Safety governance system
│ ├── session-memory-cache.json # Smart caching system
│ ├── incremental-learning-protocol.json # Efficient learning updates
│ └── learning-aware-authenticity.json # User-calibrated validation
├── examples/
│ ├── claude-preferences.txt # Example preference implementation
│ ├── knowledge-graph-export.json # Sample knowledge graph data
│ └── mcp-config-example.json # Sample MCP server configuration
└── tools/
├── framework-installer.py # Automated framework setup
├── knowledge-graph-importer.py # Import framework into memory systems
├── deploy-smart-framework.py # Token optimization deployment
└── token-optimizer.py # Usage analysis and recommendations
- Python: 3.8 or higher
- Node.js: 18 or higher (for MCP servers)
- npm: 8.19 or higher
- Operating System: macOS, Linux, or Windows with WSL
The framework requires these MCP servers to function properly:
- Memory Server:
@modelcontextprotocol/server-memory- Stores framework rules and learning data - Filesystem Server:
@modelcontextprotocol/server-filesystem- File operations and repository access - Web Search Server:
@modelcontextprotocol/server-web-searchor@modelcontextprotocol/server-ddg-search - Fetch Server:
@modelcontextprotocol/server-fetch- HTTP requests and external validation
- Claude Desktop: Version 0.7.0 or higher with MCP support enabled
- Claude API: Access to Claude with MCP configuration capability
- Other MCP-compatible AI platforms: Any AI system supporting Model Context Protocol
# Install required MCP servers
npm install -g @modelcontextprotocol/server-memory
npm install -g @modelcontextprotocol/server-filesystem
npm install -g @modelcontextprotocol/server-web-search
npm install -g @modelcontextprotocol/server-fetch
# Optional but recommended servers
npm install -g @modelcontextprotocol/server-ddg-search
npm install -g @modelcontextprotocol/server-arxiv
npm install -g @modelcontextprotocol/server-wikipedia-
Verify Prerequisites
python3 --version # Should be 3.8+ node --version # Should be 18+ npm --version # Should be 8.19+
-
Install MCP Memory Server
# Ensure you have an MCP memory server configured # This framework stores rules in persistent knowledge graphs
-
Deploy the Framework
# Interactive deployment with token optimization python tools/deploy-smart-framework.py --interactive # Or quick deployment with specific optimization python tools/deploy-smart-framework.py --optimization optimized # 70-85% token savings
-
Verify Installation
- AI should automatically begin using MCP tools
- Look for authenticity markers (🔍 VERIFIED vs
⚠️ ASSUMED) - Observe automatic tool testing at interaction start
- Monitor token usage - should see 70-85% reduction
-
Optional: Manual Installation
# For custom setups python tools/framework-installer.py
- AI proactively uses memory, filesystem, web, and analysis tools
- No explicit user prompting required
- Tools become "force multipliers" for problem-solving
- 70-85% reduction in memory queries through smart caching
- Session-based tool testing instead of per-interaction
- Batch memory updates at natural conversation breaks
- Preserves all learning capabilities with massive efficiency gains
- Claims marked as verified or assumed
- Self-audit questions prevent overconfident responses
- External verification for technical claims
- User-calibrated validation levels based on expertise
- Hierarchical rule system prevents core directive corruption
- User approval required for experimental behaviors
- Automatic rollback of harmful changes
- Emergency token conservation modes when needed
- Learns from user corrections and mistakes
- Builds predictive error prevention
- Maintains simplification bias against complexity creep
- Cross-session learning through persistent knowledge graphs
From our experiments (September 2025):
- Immediate Tool Adoption: AI began automatically using MCP tools without prompting
- Authenticity Improvement: Claims properly marked as verified vs. assumed
- Safety Validation: Successfully prevented potentially harmful experimental rules
- Learning Integration: User corrections automatically became behavioral improvements
- Token Efficiency: Solved 5-hour → 1-hour token burn through smart caching
- Cross-Platform Deployment: Successfully deployed to both Claude Code and Claude Desktop
This framework builds on:
- Behavioral Psychology: Preference simplification and habit formation
- Safety Research: Human-in-the-loop governance and fail-safe design
- AI Alignment: External observation for authenticity validation
- Systems Thinking: Hierarchical rule structures and feedback loops
- Personal AI Assistants: Proactive tool usage with safety boundaries
- Development Environments: Automatic integration with development tools
- Research Applications: Systematic use of research and analysis tools
- Content Creation: Automatic fact-checking and verification
- Business Intelligence: Proactive data analysis and reporting
This framework is the result of practical experimentation with AI behavior modification. Contributions welcome for:
- Additional safety protocols
- Integration with other MCP servers
- Performance optimizations
- Documentation improvements
MIT License - See LICENSE file for details
If you use this framework in research or production:
AI MCP Framework: Building Authentic, Proactive AI Systems
GitHub: https://github.com/simplemindedbot/ai-mcp-framework
Date: September 2025
Status: Production-ready framework validated through real-world experimentation. Last Updated: September 17, 2025