Model Context Provider (MCP) Server
Overview
what is Model Context Provider (MCP) Server?
The Model Context Provider (MCP) Server is a lightweight system designed to manage contextual data for AI models, enhancing the intelligence and responsiveness of AI applications by retrieving relevant context based on user queries.
how to use MCP Server?
To use the MCP Server, clone the repository, install the dependencies, and initialize the server in your Python environment. You can then add context data and query it as needed.
key features of MCP Server?
- 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.
use cases of MCP Server?
- Enhancing AI applications with relevant contextual information.
- Supporting chatbots and virtual assistants with dynamic context retrieval.
- Improving AI model responses based on user queries.
FAQ from MCP Server?
- What programming language is MCP Server built with?
MCP Server is built using Python.
- Can I contribute to the MCP Server project?
Yes! Contributions are welcome; you can fork the repository and submit a pull request.
- How does the context matching work?
The server uses a keyword-based search algorithm to find relevant contexts based on user queries.
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