Model Context Provider (MCP) Server

What is Model Context Provider (MCP) Server?

Model Context Provider (MCP) Server is a lightweight system for managing contextual data used by AI models. It lets applications store, update, and retrieve structured context based on user queries, improving model responsiveness. It is designed for developers building AI-driven systems that need dynamic, query-based context retrieval.

How to use Model Context Provider (MCP) Server?

Install via pip and clone the repository. Import ModelContextProvider, then use add_context to store context data with a unique ID. Query contexts using query_context with a keyword search, and feed results to the AI model via provide_model_context, which returns structured, model-ready output.

Key features of Model Context Provider (MCP) Server

• Add, update, and retrieve structured context data.
• Query-based context matching using keyword search.
• JSON-based storage for structured AI context.
• File-based context loading from external JSON files.
• Detailed debug logs for query processing.

Use cases of Model Context Provider (MCP) Server

• Provide a company’s product details and history to a customer‑support chatbot.
• Supply a virtual assistant with up‑to‑date knowledge bases for answering user questions.
• Give an AI recommendation engine relevant context from a curated set of JSON files.
• Enable multi‑turn conversations by dynamically loading context from external sources.
• Debug context retrieval logic with full query‑processing logs.

FAQ from Model Context Provider (MCP) Server

What runtime does it require?

Python 3.x and the dependencies listed in requirements.txt, installed via pip install -r requirements.txt.

How do I add context to the server?

Call mcp.add_context(context_id, content, metadata) with a unique ID and a dictionary (e.g., company info). Context is stored as JSON.

How do I find relevant context for a query?

Use mcp.query_context(query, relevance_threshold). It applies keyword‑based matching and returns the most relevant contexts.

Does it store data persistently?

The README does not specify persistent storage; it mentions JSON‑based storage and file‑based loading, implying data is held in memory and can be loaded from external files manually.

What transports or authentication does it support?

The README does not mention any transports or authentication. It is used as a Python library, not a network service.