What is MCP?

The Model Context Protocol (MCP) is an open standard designed to define how AI applications interact with external tools and data. Before MCP, connecting an AI agent to a database or a SaaS tool required manual API wiring, custom authentication handling, and specific debugging for every single connection.

MCP changes this paradigm. It allows a "Client" (the AI) to discover available tools at runtime without hardcoded definitions. It supports bi-directional communication, meaning not only can the model request data, but tools can also send event notifications back to the host.

Think of MCP as the USB-C port for AI applications. Just as a USB-C cable works regardless of the device brand, MCP ensures that the protocol between the Host and the Server remains constant, even if the underlying database changes.

The Architecture: Host, Client, and Server

The MCP architecture relies on three distinct components that enable AI to "see" and "act" in the real world.

1. MCP Host

This is the application where the AI lives (e.g., Claude Desktop, Cursor, or an AI-powered IDE). The Host provides the environment for the connection.

2. MCP Client

Living within the Host, the Client is responsible for the 1:1 communication (handshake) with the Server. It handles the transport layer and manages the request/response flow.

3. MCP Server

The Server is the bridge to the external world. It provides three specific capability sets to the AI:

1. Tools: Executable functions that allow the AI to take action (e.g., querying a database).
2. Resources: Access to data, whether local files or cloud datasets, allowing for data-driven decision-making.
3. Prompts: Pre-defined templates and workflows that make stochastic AI behavior more deterministic and reliable.

The Server also supports Slash Commands (e.g., /find-bug), which map user actions directly to specific system prompts.

The Lifecycle of an MCP Server

An MCP Server isn't just code; it has a distinct lifecycle consisting of four phases:

1. Creation: The developer defines metadata, declares capabilities, and implements the code. This is where the server's identity is established.
2. Deployment: The server is packaged and released. It can be hosted locally or remotely (e.g., via Cloudflare).
3. Operation: The runtime phase where the user interacts with the server. This involves intent analysis (matching a prompt to a tool) and execution.
4. Maintenance: Continuous monitoring, log auditing, and version control to ensure security and stability.

The Current Ecosystem and Future

The adoption of MCP is accelerating rapidly.

• Major Players: Anthropic, OpenAI (via Agent SDK), Google DeepMind, and Replit have all integrated MCP support.
• Infrastructure: Cloudflare now offers remote MCP server hosting with OAuth 2.0 support, solving some scalability and security headaches.
• Developer Tools: Frameworks like FastMCP allow developers to turn standard FastAPI endpoints into MCP tools automatically.

*This post originally appeared on my Medium

.*