Model Context Provider (MCP) Server

Overview

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

Features

• Context Management: Add, update, and retrieve structured context data.
• Query-Based Context Matching: Identify relevant contexts using a keyword-based search algorithm.
• JSON-Based Storage: Handles structured AI context data.
• File-Based Context Loading: Load context dynamically from external JSON files.
• Debugging Support: Provides detailed debug 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

1. Initialize 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", "Smart Analytics", "Prediction Engine"],
        "mission": "To 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 Context to AI Model

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

API Methods

Method	Description
add_context(context_id, content, metadata)	Adds or updates a context.
get_context(context_id)	Retrieves context by ID.
query_context(query, relevance_threshold)	Finds relevant contexts based on a query.
provide_model_context(query, max_contexts)	Returns structured model-ready context.

Contributing

We welcome contributions! If you want to improve MCP Server, feel free to fork the repo and submit a pull request.