The Model Context Protocol (MCP) is an open standard that lets an AI agent read external tools and data through one consistent interface. Instead of building a custom integration for every app, an agent speaks MCP once and can reach anything that exposes an MCP server. Suda uses it to serve your company context to any agent. Here is what the protocol is and why it caught on so fast.

The problem MCP solves

Before MCP, every connection between an AI agent and an outside system was bespoke. Want your agent to read Notion? Build a Notion integration. Add Slack? Build another. Each one had its own auth, its own shape, its own quirks. The work grew with every tool, and none of it transferred.

MCP standardizes the connection. The agent learns one protocol, and any system that exposes an MCP server becomes reachable. The integration work moves to the server side and gets written once, not once per agent.

How MCP works, briefly

There are two roles: a client and a server.

The client is the AI application, such as Claude or an agent you build. It speaks MCP and can call any server it is connected to.
The server exposes a capability: a set of tools the client can call, or data it can read. A server might wrap a database, a ticketing system, or a whole context graph.

When the agent needs something, it calls a tool on the server, gets a structured result, and uses it in its answer. The agent does not need to know how the server works inside. It only needs the protocol.

What you can connect over MCP

Because the protocol is open, the ecosystem is broad. Common servers expose:

Single tools. One app or database per server, such as an MCP server for a specific ticketing system.
Actions. Servers that let the agent do things, not just read, like creating a record.
Context. Servers that hand the agent connected, current context across many tools at once.

That last category is where the leverage is. A context server does the cross-tool work before the agent asks, so the agent gets one coherent answer instead of a pile of raw lookups. That is the role Suda plays. Read MCP context servers for how that differs from a per-tool server.

Why MCP matters for context

An agent is only as good as the context it can reach. MCP is what makes that context reachable without custom glue. But the protocol only moves the data. What you put on the server side decides answer quality.

Connect ten single-tool servers and the agent still has to pick the right one, merge results, and guess which fact is current. Connect one context server and that work is already done: the facts are reconciled, stale ones retired, conflicts resolved. Suda builds that context graph and serves it over MCP, so any MCP client reads your real, current context.

npx suda connect

Common questions

Is MCP tied to one AI company? No. It is an open protocol, so any client or server can implement it. That is why the ecosystem grew across tools rather than inside one product.

Do I need to write code to use it? To expose a custom server, yes. To use existing servers, usually not. With Suda you run one command and point your agent at the result.

How does MCP lower cost? It does not by itself. A good context server does, by sending only the context a question needs. For Suda that is about 85% less text per answer, which lowers token cost.

Getting started

The fastest way to see MCP do something useful is to give an agent your real context. Connect a source and point your agent at it:

npx suda connect

To wire it to a specific assistant, read how to give Claude your company context over MCP.