Adaptive MCP Server

Overview

The Adaptive MCP (Model Context Protocol) Server is an advanced AI reasoning system designed to provide intelligent, multi-strategy solutions to complex questions. By combining multiple reasoning approaches, real-time research, and comprehensive validation, this system offers a sophisticated approach to information processing and answer generation.

Key Features

• Multi-Strategy Reasoning

  • Sequential Reasoning
  • Branching Reasoning
  • Abductive Reasoning
  • Lateral (Creative) Reasoning
  • Logical Reasoning

• Advanced Research Integration

  • Real-time information retrieval
  • Multiple search strategy support
  • Confidence-based result validation

• Comprehensive Validation

  • Semantic similarity checking
  • Factual accuracy assessment
  • Confidence scoring
  • Error detection

Installation

Prerequisites

• Python 3.8+
• pip
• Virtual environment recommended

Setup

# Clone the repository
git clone https://github.com/your-org/adaptive-mcp-server.git

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

# Install dependencies
pip install -r requirements.txt

Quick Start

Basic Usage

from reasoning import reasoning_orchestrator

async def main():
    # Ask a complex question
    result = await reasoning_orchestrator.reason(
        "What are the potential long-term impacts of artificial intelligence?"
    )
    
    print(result['answer'])
    print(f"Confidence: {result['confidence']}")

Configuration

Create a mcp_config.json in the project root:

{
    "research": {
        "api_key": "YOUR_EXA_SEARCH_API_KEY",
        "max_results": 5,
        "confidence_threshold": 0.6
    },
    "reasoning": {
        "strategies": [
            "sequential", 
            "branching", 
            "abductive"
        ]
    }
}

Advanced Usage

Custom Reasoning Strategies

from reasoning import reasoning_orchestrator, ReasoningStrategy

# Customize strategy selection
custom_strategies = [
    ReasoningStrategy.LOGICAL, 
    ReasoningStrategy.LATERAL
]

# Use specific strategies
result = await reasoning_orchestrator.reason(
    "Design an innovative solution to urban transportation",
    strategies=custom_strategies
)

Development

Running Tests

# Run all tests
pytest tests/

# Run specific module tests
pytest tests/test_research.py
pytest tests/test_orchestrator.py

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Best Practices

1. Modularity: Leverage the modular design to extend reasoning capabilities
2. Confidence Scoring: Always check the confidence field in results
3. Error Handling: Implement try-except blocks when using the reasoning system
4. API Key Management: Use environment variables for sensitive configurations

Troubleshooting

• Ensure all dependencies are installed
• Check your Exa Search API key
• Verify network connectivity
• Review logs for detailed error information

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Your Name - your.email@example.com

Project Link: https://github.com/your-org/adaptive-mcp-server