Model Context Provider (mcp) Server O2k

🚀 Model Context Provider (MCP) Server

The Model Context Provider (MCP) Server is a lightweight and efficient system designed to manage context data for AI models. It helps AI applications retrieve relevant information based on user queries, thereby enhancing the overall intelligence and responsiveness of AI-driven systems.

🚀 Quick Start

The Model Context Provider (MCP) Server is a lightweight and efficient system that manages context data for AI models. It enables AI applications to retrieve relevant information based on user queries, enhancing the overall intelligence and responsiveness of AI-driven systems.

✨ Features

• Context Management: Add, update, and retrieve structured context data.
• Query-based Context Matching: Use keyword search algorithms to identify relevant contexts.
• JSON Storage: Handle structured AI context data.
• File Loading Context: Dynamically load contexts from external JSON files.
• Debugging Support: Provide detailed debugging logs for query processing.

📦 Installation

To install and run the MCP server, follow these steps:

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

# Install dependencies
pip install -r requirements.txt

💻 Usage Examples

Basic Usage

1. Initialize the MCP Server

from mcp_server import ModelContextProvider

mcp = ModelContextProvider()

2. Add Context

mcp.add_context(
    "company_info",
    {
        "name": "TechCorp",
        "founded": 2010,
        "industry": "Artificial Intelligence",
        "products": ["AI Assistant", "Intelligent Analysis", "Prediction Engine"],
        "mission": "Make AI accessible to everyone"
    }
)

3. Query Context

query = "What are the features of the AI Assistant product?"
relevant_context = mcp.query_context(query)
print(relevant_context)

4. Provide Model Context

model_context = mcp.provide_model_context(query)
print(model_context)

📚 Documentation

API Methods

💪 Contribution

We welcome contributions! If you'd like to improve the MCP server, feel free to fork the repository and submit a pull request.