Model Context Protocol (MCP)

Why It Matters

Before MCP, every AI application had to build custom integrations for each data source and tool. If you wanted your AI to access Slack, GitHub, and your database, you’d build three separate integrations with different authentication patterns, data formats, and error handling. Multiply that across thousands of possible tools and the maintenance burden becomes unsustainable.

MCP solves this with a universal protocol. Think of it like USB-C replaced the mess of proprietary chargers. MCP provides a single standard way for AI applications to connect with any compatible tool or data source. Build the integration once, and it works across any MCP-compatible AI application.

For AI engineers, MCP changes how you architect systems. Instead of tightly coupling your AI application to specific integrations, you build against a standard protocol. Your application gains access to a growing ecosystem of pre-built MCP servers (from file systems to databases to SaaS APIs) without custom integration work.

How It Works

MCP uses a client-server architecture with three core components:

MCP Hosts These are AI applications (like Claude Desktop or IDE extensions) that want to access external data. The host runs an MCP client that manages connections to servers.

MCP Clients The client component handles the protocol communication. It maintains connections to MCP servers, routes requests, and manages the lifecycle of server connections.

MCP Servers Lightweight programs that expose specific capabilities, such as reading files, querying databases, and calling APIs. Each server implements the MCP protocol to describe what it can do and handle requests.

When a user asks the AI to perform a task, the host queries connected MCP servers for available tools and context. The server responds with what it can provide, and the AI can then invoke those capabilities through structured requests.

Implementation Basics

Getting started with MCP involves either using existing servers or building your own:

Using Pre-built Servers The MCP ecosystem includes servers for common use cases: filesystem access, Git operations, web browsing, database queries. You configure your MCP host (like Claude Desktop) to connect to these servers, and they become available to the AI.

Building Custom Servers For proprietary systems or custom workflows, you can build MCP servers in Python or TypeScript using the official SDKs. A server defines the tools it exposes, their input schemas, and handlers for executing requests.

The protocol handles capability negotiation, so clients and servers can evolve independently. A server can advertise new capabilities without breaking existing clients, and clients can request only the capabilities they understand.

Start with the official MCP documentation and existing server implementations. The ecosystem is growing rapidly, with community contributions covering an expanding range of use cases.