What does MCP stand for?

MCP stands for Model Context Protocol — an open standard for connecting AI models and agents to external tools, data, and context sources.

What is an MCP server?

An MCP server is a program that exposes tools, resources, or prompts over the Model Context Protocol. An AI client (like Claude Code or Cursor) connects to it and can call those tools during a task.

How is MCP different from RAG?

RAG retrieves documents and stuffs them into a prompt. MCP is a live protocol: the agent calls tools on demand and can also take actions, not just read text. They're complementary.

MCP (Model Context Protocol)

MCP is an open protocol that lets AI applications connect to external tools and data through one standard interface — agents fetch context and act on demand.

The Model Context Protocol (MCP) is an open standard that gives AI applications a uniform way to connect to external systems. Instead of building a bespoke integration for every model-to-tool pairing, a developer exposes an MCP server, and any MCP-speaking client — such as Claude Code or Cursor — can discover and call its tools.

Why it matters for coding agents

A coding agent is only as good as the context it can reach. MCP lets an agent pull context on demand rather than depending on whatever was pasted into the prompt. For example, a get_product_context tool can return the approved decisions relevant to the current task — so the agent starts already knowing the constraints instead of rediscovering them one broken pull request at a time.

Core concepts

Tools — functions the agent can call (e.g., fetch decisions, run a query).
Resources — data the server exposes for the model to read.
Prompts — reusable prompt templates the server offers.

How Lockstep uses MCP

Lockstep exposes an MCP server so agents can fetch the team's approved decisions before they write code, and so drift can be caught when generated code conflicts with them. Read more in context engineering and The Feed Is Coming for GitHub.