Overview
What is Model Context Protocol (MCP)?
Model Context Protocol (MCP) is a protocol designed to establish a communication channel between large language models (LLMs) and external tools or data sources, utilizing a client-server architecture and JSON-RPC 2.0 for messaging.
How to use MCP?
To use MCP, set up an MCP server using FastMCP or Low-Level APIs, and implement a client that communicates with the server via SSE or stdin. The server provides tools, resources, and prompts for the client to utilize.
Key features of MCP?
- Client-server architecture for communication between LLMs and external tools.
- Supports JSON-RPC 2.0 for messaging.
- Provides capabilities like tools, resources, and prompt templates.
Use cases of MCP?
- Integrating LLMs with educational tools for language learning.
- Enabling LLMs to access external data sources for enhanced responses.
- Facilitating remote procedure calls between clients and servers in AI applications.
FAQ from MCP?
- What is the purpose of MCP?
MCP enables seamless communication between LLMs and external tools, enhancing their functionality.
- Is MCP easy to implement?
Yes, MCP provides both high-level and low-level APIs for flexibility in implementation.
- Can MCP be used for various applications?
Yes, MCP is versatile and can be adapted for different use cases in AI and data processing.