The Protocol Layer for AI Agents: Inside the Model Context Protocol (MCP) (1/2)
The emerging standard that turns language models into system-level actors.
This is post 1 of a 2 part series (see 2/2 here)
Section 1: Why Protocols Matter Now
Everyone is building AI agents. But almost no one is asking the deeper question: How should agents interact with the world?
Today, most agents rely on brittle wrappers and hardcoded toolchains. A developer stitches together a workflow on a platform and prays that things keep working—new platform/tool equals new integration. Every integration is bespoke. Every workflow is fragile.
This doesn’t scale. Not in terms of engineering effort. Not in terms of reliability. And certainly not in terms of reasoning.
What we need isn’t more platforms with custom integrations.
What we need is protocol thinking.
Just as HTTP standardized how browsers fetch and render content, the AI ecosystem needs a protocol to standardize how agents access tools, data, and workflows. That’s where the Model Context Protocol (MCP) comes in.
MCP is not a product. It’s not a dev tool. It’s a design shift. A shared grammar for agent-to-environment interaction.
This article is Part 1 of a two-part series exploring MCP from the perspective of someone seriously unpacking its potential. I won’t give you a hello world or an SDK demo. Instead, I’ll walk you through the ideas, primitives, and strategic consequences of thinking protocol-first.
In Part 2, we’ll dive into design insights, security risks, and what protocol-native systems might look like in the wild.
If you’re building in the agent space, MCP might be the most important thing you haven’t looked at yet.
Let’s fix that.
Section 2: What Is MCP, Really?
At its core, the Model Context Protocol (MCP) is an **open standard** for how LLMs interact with the outside world.
It’s not a new interface for humans. It’s a grammar for AI-to-environment interaction—focused on enabling models to reason, plan, and act.
Here’s how it works:
MCP operates on a client-server model. The AI agent (client) initiates a task and communicates with one or more MCP servers, each of which exposes specific capabilities. These capabilities come in three distinct forms:
1. Tools – executable functions. Think `search_web`, `trigger_build`, `create_task`, `send_email`. These are the verbs—the actions the agent can take.
2. Resources – structured context. Think of these as data the model can query, summarize, reason over. Logs, documents, search results, configs, memory.
3. Prompts – templated workflows. These are starting points: “summarize this conversation,” “set up a CI pipeline,” “craft an outreach email.”
The power of MCP isn’t just in what it enables, but *how* it enables it: each tool or resource comes with a semantic description written in natural language. That means the model can reason about what’s available, dynamically.
This makes MCP a model-facing protocol. It’s designed not just for interop between software components, but for semantic discoverability by language models.
And that’s what sets it apart from frameworks like REST or GraphQL: those are made for developers. MCP is made for agents.
Next, let’s look at why this model-first design unlocks a new scale of integration.
Section 3: The Strategic Payoff — Solving the M×N Problem
Let’s say you have 5 AI clients—maybe a chatbot, an IDE agent, a calendar assistant, a customer support bot, and a testing assistant.
Now imagine you want to connect them to 10 different tools: Jira, Gmail, Jenkins, Salesforce, Notion, GitHub, Slack, Stripe, Zoom, and your internal API gateway.
Without a shared protocol, you’re looking at a 5 × 10 matrix of one-off integrations. That’s 50 glue-code pipelines to maintain. Fifty different ways to break. Fifty reasons for your team to hate their life.
This is the M×N integration problem. Every new model or tool multiplies the integration cost.
MCP collapses that problem.
Tools implement the protocol once.
Clients speak the protocol once.
Integration cost becomes M + N, not M × N.
This is what makes MCP more than just convenient. It’s infrastructure leverage. The kind you build ecosystems on top of.
When people say, “MCP is like USB-C for AI,” this is what they’re getting at: a single standardized interface that lets everything plug into everything else—securely, semantically, and scalably.
This is what protocol-native foundations look like.
And it opens up new design space entirely.
Next up: what this looks like in practice.
Section 4: Designing with MCP — A Gmail Agent Sketch
Let’s say you’re building an AI assistant that helps manage your Gmail inbox: triaging emails, replying to common threads, and pulling in relevant docs.
Here’s what exposing Gmail via MCP might look like:
Tools `send_email`, `reply_to_thread`, `archive_message`, `search_inbox`
Resources full message content, thread history, email metadata
Prompts “summarize this thread,” “compose a polite follow-up,” “respond with a calendar link”
Now the agent doesn’t need hardcoded API calls. It can dynamically:
Discover capabilities
Reason over semantic tool descriptions
Invoke actions through the shared grammar of MCP
This is what makes the design protocol-native.
Instead of embedding business logic in your orchestration layer, you externalize capability semantically. The agent orchestrates based on intent and environment—not brittle flows.
Even more interesting: multiple agents (say, a sales agent and an exec assistant) could share access to the same Gmail MCP server, yet interact with it entirely differently—based on their role, memory, and task.
This kind of decoupling is a hallmark of protocol-native infrastructure.