zhifengzhang-sz/mcp-server

AI Code Generation Consistency Study

A sophisticated AI consistency study platform using TypeScript/Bun stack to measure AI code generation consistency across different models, focusing on Haskell code generation.

🎯 Project Overview

This study implements a 4-layer functional programming architecture for measuring AI code generation consistency:

1. Interfaces - Pure type definitions and contracts ✅ COMPLETE
2. Core - Mathematical foundations (Result, QiError) ✅ COMPLETE
3. Components - QiPrompt (prompt engineering) + QiAgent (AI providers) ✅ COMPLETE
4. Application - Study orchestration and analysis ✅ COMPLETE

📚 Documentation Architecture

All documentation is now organized in /docs following proper naming conventions:

docs/
├── contracts/         # Component interface contracts  
├── arch/             # System architecture and design
├── guides/           # User and developer guides
└── impl/             # Implementation analysis and alignment

👉 Start with: for complete documentation index

Documentation Status: ✅ COMPLETE AND IMPLEMENTATION-READY

✨ Key Features

• 🚀 Modern Tech Stack: Bun runtime (3x faster than Node.js), Biome linter (10-100x faster than ESLint+Prettier)
• 🤖 AI Integration: Claude Code CLI/SDK integration with multiple provider support
• 📐 Mathematical Foundation: QiCore v4.0 base components with Result monad and QiError system
• 📊 Study Platform: Comprehensive code generation analysis with quality metrics
• 🔧 Package-Based: Following QiCore v4 TypeScript specification with proven libraries

🚀 Quick Start

Prerequisites

• Bun 1.2.0+ (JavaScript runtime)
• TypeScript 5.3.0+
• Claude Code CLI (for AI integration)

Installation

# Clone the repository
git clone https://github.com/your-org/ai-consistency-study.git
cd ai-consistency-study

# Install dependencies with Bun
bun install

# Run development server
bun dev

Development Commands

# Run tests
bun test

# Run linting
bun run lint

# Generate study results
bun src/generators/run-study.ts

# Start analysis dashboard
bun src/dashboard/server.ts

🏗️ Architecture Highlights

Core Components (Phase 1)

• 🔌 Plugin System: Extensible plugin architecture with discovery, composition, and lifecycle management
• 📋 Context Assembly: Immutable context management with plugin-based enhancement
• 🛠️ Tool Registry: Functional tool registry with security and performance adapters
• 📊 Session Management: Event-sourced session management with immutable state
• 🤖 LLM Integration: Provider-agnostic LLM interface with pipeline processing
• ⚙️ Configuration: Hierarchical configuration management with validation

Key Architectural Patterns

• Functional Programming: Immutable data structures, pure functions, composition patterns
• Plugin Architecture: Extensible through adapter pattern with clear extension points
• Event Sourcing: Session state management through event streams
• Language Agnostic: Design specifications use mathematical notation, not code syntax
• Type Safety: Complete type annotations and validation throughout

📖 Documentation Deep Dive

For Developers

• : Complete development environment setup
• : Production-ready implementation documentation
• : System architecture and integration strategies

For Architects

• : Language-agnostic component designs
• : 4-phase evolution strategy
• : Protocol integration approach

For Product Managers

• : Phase-by-phase capability definitions
• : Current phase capabilities and goals

🔍 Quality Assurance

Documentation Verification Framework

We maintain comprehensive documentation consistency through automated and manual verification:

• Language-Agnostic Compliance: No code syntax in design specifications
• Cross-Layer Consistency: Complete traceability from objectives to implementation
• Interface Integrity: Consistent interfaces across all documentation layers
• Extension Point Continuity: Plugin extensibility preserved throughout all phases

Verification Tools

# Run all verification checks
./scripts/verify_all.sh

# Check specific consistency layers
./scripts/verify_language_agnostic.sh
./scripts/verify_interface_consistency.sh
./scripts/verify_cross_references.sh

Documentation Quality Gates

• ✅ 100% Implementation Coverage: All design components have implementation specs
• ✅ Complete Traceability: Objective → Architecture → Design → Implementation
• ✅ Interface Consistency: Identical signatures across all layers
• ✅ Production Ready: Type safety, error handling, performance optimization

🛣️ Roadmap

Phase 1: Plugin-Ready Foundation ✅ COMPLETE

• Plugin system with discovery and composition
• Context assembly with enhancement capabilities
• Tool registry with security and performance layers
• Event-sourced session management
• MCP protocol integration
• Complete documentation framework

Phase 2: RAG Integration 🔄 NEXT

• Vector store integration
• Knowledge base management
• Context enhancement pipelines
• Information retrieval systems

Phase 3: sAgent Coordination 🔄 PLANNED

• Multi-agent coordination
• Agent communication protocols
• Task delegation and orchestration
• Agent discovery and registration

Phase 4: Autonomous Capabilities 🔄 PLANNED

• Autonomous decision making
• Learning and adaptation
• Goal-oriented behavior
• Self-monitoring and adjustment

🤝 Contributing

We welcome contributions! Please see our for details.

Development Workflow

1. Documentation First: All changes start with documentation updates
2. Consistency Verification: Run verification scripts before committing
3. Cross-Layer Review: Ensure changes maintain traceability across all layers
4. Extension Points: Preserve plugin extensibility in all modifications

Code Quality Standards

• Type Safety: Complete type annotations required
• Functional Patterns: Immutable data structures and pure functions
• Plugin Extensibility: All components must support plugin extensions
• Documentation: Changes must update all relevant documentation layers

📄 License

This project is licensed under the MIT License - see the file for details.

🙏 Acknowledgments

• qicore-v4: Mathematical framework and verification methodologies
• MCP Protocol: Model Context Protocol specification and community
• Functional Programming Community: Patterns and best practices

Status: Phase 1 Complete - Implementation Ready 🚀
Documentation: Complete 4-layer architecture with verification framework
Next: Phase 2 RAG Integration planning and implementation