MCP Protocol Introduction and Related Resources

What is MCP?

MCP is an open protocol that standardizes how applications provide context information to large language models (LLMs). Think of MCP as a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.

Simply put, the Model Context Protocol (MCP) is an open standard designed to enable cutting-edge AI models to generate better, more relevant responses by accessing the necessary data. Imagine it as a universal "connector" that replaces the cumbersome process of custom development for each data source, providing a more scalable and reliable way to feed AI systems the information they need.

Developers can leverage MCP in two primary ways:

• Expose data via an MCP server: Allows data sources to communicate with AI systems in a standardized manner.
• Build AI applications (MCP clients) that connect to these servers: Enables AI tools to easily access the required data.

At its core, MCP is an open specification, meaning anyone can use and implement it. This openness is crucial for driving wider adoption and fostering interoperability within the AI ecosystem.

MCP Architecture

MCP follows a client-server architecture where a single host application can connect to multiple servers:

• MCP Host: Programs such as the Claude desktop app, IDEs, or AI tools that wish to access data through MCP.
• MCP Client: A protocol client that maintains a 1:1 connection with the server.
• MCP Server: Lightweight programs, each exposing specific functionality via a standardized model context protocol.
• Local Data Sources: Your computer files, databases, and services securely accessible to MCP servers.
• Remote Services: External systems accessible via the internet (e.g., through APIs) that MCP servers can connect to.

MCP's Powerful Capabilities

The core of MCP lies in its function as a single, universal, and open standard for connecting AI systems to diverse data sources. This unification drastically simplifies the data connection process, eliminating the need for developers to build and maintain separate connectors for each data source.

More importantly, MCP not only allows AI systems to access data but also supports developers in establishing secure two-way communication between their data repositories and AI-powered tools. This means AI can not only read information but may also be able to interact with or update data within systems, enabling more complex workflows.