MCP-Chinese-Getting-Started-Guide
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The Model Context Protocol (MCP) is a groundbreaking open-source protocol enabling large language models to seamlessly connect with various external data sources and tools. It provides a standardized method for AI models to interact with their environment, much like a USB-C interface for AI applications.
## Overview Model Context Protocol (MCP) is an innovative open-source protocol redefining how large language models (LLM) interact with the external world. MCP standardizes the method for LLMs to connect with various data sources and tools, facilitating seamless information access and processing. It serves as a USB-C interface for AI applications, offering standardized connectivity for AI models. ### Core Features - Resources - Prompts - Tools - Sampling - Roots - Transports ### Development Guide 1. **MCP Server**: The document provides a guide to developing an MCP server for network search using Python and tools like uv and FastMCP. 2. **MCP Client**: Details on implementing the MCP client to interact with the server are included. 3. **Sampling**: An explanation of the sampling functionality, allowing pre-execution and post-execution operations. ### Usage - Utilizes stdio and SSE for data transfers, with SSE used for cloud deployments. - Recommended for AI applications needing standardized data and tool connectivity. - Examples provided for integrating with tools like DeepSeek and LangChain. ### Deployment - Instructions for deploying MCP services to the cloud using serverless platforms, particularly with SSE protocol.
