The Model Context Protocol (MCP) is an open standard that lets AI models call external tools and read external data through a single, consistent interface. Think of it as a universal adapter between an AI agent and the systems it needs to act on.
Before MCP, every AI application wired up its integrations by hand. Each model provider, each tool, and each data source had its own bespoke glue code. MCP replaces that one-off wiring with a shared protocol: a client (the AI app) talks to one or more servers (the tools), and any compliant client can use any compliant server. It is, in spirit, what the Language Server Protocol did for code editors, applied to AI agents.
How MCP works
MCP defines three roles. A host is the application a person interacts with, such as a coding agent or a chat client. Inside the host runs an MCP client, which opens a connection to an MCP server. The server exposes capabilities, and the client makes them available to the model.
A server can offer three kinds of capability. Tools are functions the model can call, each with a typed input schema, such as "create an issue" or "query this table." Resources are readable data the model can pull in as context, like a file or a record. Prompts are reusable templates a user can invoke. The transport underneath is usually JSON-RPC over stdio for local servers or HTTP with server-sent events for remote ones.
The flow is simple. The client asks the server what it offers. The server lists its tools and their schemas. The model, seeing those schemas, decides when to call a tool and with what arguments. The client forwards the call, the server executes it, and the result comes back as context the model can reason over. The model never touches credentials or runs code directly; the server owns that boundary.
Why MCP matters for multi-agent coding
A single agent with no tools can only talk. The moment you want it to read your database, open a pull request, or check what a teammate changed, it needs to reach outside its own context window. MCP is how it reaches out in a standard way, which means three things that matter for teams.
First, portability. Write an MCP server once and any MCP-aware agent can use it: Claude Code, Cursor, Codex, and others all speak the protocol. You are not locked into a single vendor's plugin format. Second, composability. An agent can connect to several servers at once, mixing a code-search server, a database server, and a coordination server in one session. Third, a clean security boundary. Tools run in the server, not the model, so you decide exactly what an agent is allowed to do and what data it can see.
MCP does not give agents memory of each other. The protocol moves tool calls and data between one agent and one server. Sharing state across agents and machines is a layer you build on top of MCP, which is exactly where a coordination server comes in.
How Aethereum uses MCP
Aethereum is an MCP server that gives a team's agents a shared brain. Instead of exposing files or a database, it exposes three coordination tools that every connected agent can call:
share_intent(text) records what an agent is building right now. declare_contract(name, shape, dependsOn?) registers or updates an interface contract, such as an API signature or a shared type. get_team_context()returns teammates' current intent, the latest contracts, and alerts for any contract that changed under an agent's feet.
Because these are ordinary MCP tools, any MCP-aware agent on any machine can use them without special integration. When one developer's agent declares a new contract shape, every other developer's agent receives that change as live context the next time it calls get_team_context(), even though they run on different laptops in different cities. That cross-machine, uncommitted-contract case is the gap Aethereum fills.
$ npx aethereum init# writes the MCP server config for your agent,# then your agent can call get_team_context() on its ownFor the full tool surface, transport details, and setup for each agent, see the documentation. To see how a contract change becomes a collision alert before a pull request, read why AI coding agents create merge conflicts.
Takeaway
MCP is the standard plug between an AI agent and the outside world: tools it can call, resources it can read, all through one protocol that any compliant agent understands. Its real power for teams is composition. Stack a coordination server like Aethereum on top, and the same protocol that lets one agent read a file lets a whole team of agents stay on the same page across machines.