ksrk-mcp-server-client
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This document provides a comprehensive guide to setting up and using a Model Context Protocol (MCP) server with Python 3.13, including installation, usage, and project file descriptions.
The Model Context Protocol (MCP) server is designed to facilitate interactions between a client and the OpenAI GPT-4 model, leveraging web scraping capabilities through the ScrapingDog API. The server is implemented in Python and requires setting up a virtual environment and installing necessary dependencies. The project includes a client script that interacts with the MCP server, allowing users to input prompts and receive responses processed by the LLM and available tools. The server also includes functionalities for web searching and content fetching, enhancing the model's ability to provide contextually relevant information.
Features
- Interactive Client: Allows users to input prompts and receive responses in a loop, with the ability to quit or exit.
- Web Scraping: Utilizes the ScrapingDog API to search the web and fetch content from URLs.
- Tool Management: The MCPClient class manages connections and provides methods to retrieve and call available tools.
- Asynchronous Processing: Uses asynchronous functions for efficient web searching and content fetching.
- Environment Configuration: Supports environment variable setup for API keys and other configurations.
