MCP Core Concepts - Resources, Tools, Prompts & Transports!
AI Summary
The video introduces the core concept of MCP (Model Context Protocol), an open protocol that standardizes how applications provide context to large language models (LLMs). MCP enables AI agents and complex workflows on top of LLMs by integrating proprietary tools, databases, and data as relevant context to improve responses. It establishes a standardized approach to connect LLM clients (like Claude) to MCP servers, which access various data sources such as databases, APIs, and files.
Key components of MCP include:
- Resources: Data endpoints similar to HTTP GET requests, exposing relevant context data without side effects.
- Tools: Executable functions allowing LLMs to perform real-world actions with side effects, such as sending emails or automating browsers.
- Prompts: Reusable, user-controlled templates that improve interaction with LLMs by guiding workflows and including dynamic input.
The protocol uses JSON-RPC 2.0 over transports such as standard input/output streams for local integration and streamable HTTP for web integrations. MCP supports flexible vendor switching, secure data handling, and stateful session management.
The video also references a practical example of an MCP server built with Python integrating a Postgres database and file system resources. Viewers are encouraged to support the channel and explore membership for additional content.