The MCP Server provides a lightweight framework for managing and querying contextual data for AI models. It supports creating, updating, and retrieving structured context entries, and includes a keyword-based search to identify relevant contexts for a given user query. Contexts can be loaded from external JSON files, enabling dynamic updates without modifying the server code. Tools exposed by the server allow you to add contexts, query for relevant contexts, and prepare model-ready context data for AI models. This makes it easier to supply AI models with targeted backgrounds and facts during inference.

Prerequisites:

• Python 3.8 or newer
• pip (comes with Python)

Installation steps:

1. Clone the repository (or download the source): git clone https://github.com/your-repo/mcp-server.git cd mcp-server

2. Create a virtual environment (optional but recommended): python -m venv env source env/bin/activate # on macOS/Linux .\env\Scripts\activate # on Windows

3. Install dependencies: pip install -r requirements.txt

4. Run the MCP Server: python -m mcp_server

5. (Optional) Load initial context from a JSON file if supported by your setup:

   Example: mcp_server loads contexts from a JSON file upon startup

   export MCP_CONTEXTS_PATH=/path/to/contexts.json python -m mcp_server

Notes and tips:

• The server uses JSON-based storage for contexts and supports file-based loading of contexts to keep data in sync with external sources.
• Use the provided API methods to add or update contexts, retrieve specific contexts, and query for relevant contexts based on keywords.
• Enable verbose debugging in the environment if you need detailed logs during query processing.
• If you plan to load large context datasets, consider batching the load and monitoring memory usage.
• Environment variables can be used to control behavior (e.g., paths to JSON context files, logging level, and query relevance thresholds).