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Adaptive_mcp_server

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# 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

```bash

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

```python 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: ```json { “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

```python 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

```bash

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\](https://github.com/your-org/adaptive-mcp-server)