MCP Protocol Deep Dive: The Universal Agent Interface

Before MCP, every AI tool integration was a snowflake. You wanted your agent to query a database? Write a custom integration for Claude. Then write a different one for GPT. Then write a third one for Gemini. Three implementations of the same functionality, each with its own bugs and limitations.

MCP ends this. The Model Context Protocol is to AI agents what HTTP was to web browsers. A standard interface that means you build once and it works everywhere.

And if that sounds like marketing fluff, let me be concrete. I've built MCP servers. I've consumed them from multiple AI platforms. The protocol works, and it changes how you think about building AI-powered tools.

What MCP Actually Is

MCP defines a client-server architecture. The AI agent is the client. Your tools are the server. The protocol specifies how the client discovers what the server can do, how it calls those capabilities, and how it handles the responses.

An MCP server exposes three types of capabilities:

Tools. Functions the agent can call. "Query the database," "send an email," "create a file," "deploy to production." Each tool has typed parameters, a description, and a return type. The agent reads the description, decides when to use the tool, and calls it with appropriate arguments.

Resources. Data the agent can read. "The contents of this file," "the current database schema," "the list of active users." Resources are read-only. The agent can reference them for context but can't modify them through the resource interface.

Prompts. Predefined prompt templates the server provides. "Summarize this document," "review this code for security issues," "generate a test for this function." Prompts give the server influence over how the agent approaches specific tasks.

The separation between tools, resources, and prompts is deliberate. Tools are for actions. Resources are for context. Prompts are for guidance. Each has different security implications and different usage patterns.

Building an MCP Server

Building an MCP server is surprisingly straightforward. The protocol handles the complexity. You focus on your tool's logic.

Here's the mental model. You write a function that does something useful. You describe that function with a name, a description, and typed parameters. You register it with an MCP server framework. The framework handles discovery, serialization, error reporting, and transport.

Transport is flexible. MCP supports stdio (for local tools that run as child processes) and HTTP with server-sent events (for remote tools). Stdio is simpler and faster for development. HTTP is better for production services that need to scale independently.

The framework I use most is the official TypeScript SDK. You define your tools as functions, add parameter validation with Zod, and the SDK generates the MCP endpoint. Total setup for a basic server is maybe 50 lines of code.

For more complex servers, you add authentication (who can use this tool), rate limiting (how often can they use it), and logging (what did they do with it). These are the same concerns you'd have for any API, and MCP doesn't reinvent the wheel here. Use your existing auth middleware, your existing rate limiter, your existing logger.

The Composability Argument

This is where MCP gets genuinely powerful. An AI agent can connect to multiple MCP servers simultaneously.

Server 1: A database server that exposes query and mutation tools. Server 2: A file system server that exposes read and write tools. Server 3: A web scraping server that exposes fetch and parse tools. Server 4: A deployment server that exposes build and deploy tools. Server 5: A monitoring server that exposes metrics and alert tools.

The agent sees all of these tools in a unified interface. It can read data from the database, write it to a file, scrape a webpage to compare against the data, and deploy the changes. All in a single workflow, orchestrated by the agent, using tools from five different servers.

This composability is the real value proposition. Each MCP server is a building block. Combine blocks to create capabilities that no single tool provides. And because the interface is standard, adding a new block is trivial.

Compare this to the old approach: a custom integration for each tool, each with its own SDK, its own authentication, its own error handling, its own data format. MCP collapses all of that into a uniform interface.

Discovery and Capability Negotiation

When an agent connects to an MCP server, the first thing it does is ask "what can you do?" The server responds with a list of its tools, resources, and prompts. The agent reads the descriptions and understands what's available.

This discovery mechanism is what makes MCP agents adaptable. Connect a new server, the agent discovers new capabilities. Disconnect a server, the agent adapts to the reduced capability set. No code changes needed.

Capability negotiation extends this further. The client and server agree on which protocol features they both support. If the server supports streaming but the client doesn't, they fall back to non-streaming mode. If the server supports authentication but the client is running locally, they skip auth.

This negotiation makes MCP forward-compatible. New protocol features can be added without breaking existing implementations. Servers and clients that support the new feature use it. Those that don't, gracefully degrade.

MCP in Production

Running MCP servers in production means thinking about reliability, security, and observability.

Reliability: MCP servers should be stateless where possible. If the server crashes and restarts, agents reconnect and rediscover capabilities. For stateful tools (like database transactions), implement proper cleanup and recovery.

Security: Validate every tool call. Just because an agent asks to delete all records doesn't mean you should do it. Implement authorization checks, input validation, and audit logging on every tool invocation. The agent is not a trusted caller. It's an intermediary that processes untrusted user input.

Observability: Log every tool call with its parameters, result, and duration. Track error rates and latency per tool. Set alerts for anomalous patterns. When something goes wrong in a multi-tool workflow, the logs are your forensic evidence.

Why MCP Will Win

The network effect. Every MCP server that gets built makes the protocol more valuable for agents. Every agent that supports MCP makes the protocol more valuable for server builders. This flywheel is already spinning.

Anthropic launched MCP, but adoption extends well beyond Claude. OpenAI supports it. Google supports it. Open-source agents support it. The protocol is not locked to any single vendor, which removes the biggest adoption barrier.

In two years, building an AI tool without MCP support will be like building a website without HTTP support. Technically possible, but why would you?