Model Context Protocol (MCP) Explained

The Model Context Protocol is an open specification, originally released by Anthropic, that defines how an AI client, like Claude Desktop, Cursor, or Claude Code, talks to an external server exposing tools, resources, and prompts. Think of it as a common language: instead of every AI application inventing its own way to call a database or a search API, both sides speak the same protocol and any client can use any server.

Practically, the model context protocol defines a small set of primitives: tools the model can call (like "search memory" or "create a ticket"), resources it can read (like a file or a database record), and prompts a server can suggest. A client connects to a server, asks what it offers, and the model decides when to use each capability during a conversation.

An MCP connection has two sides: a host application (the AI client) and one or more MCP servers it connects to. On startup, the client asks each connected server to list its available tools and resources. When the model decides a tool is relevant to the current request, the client sends a structured call to the server, waits for the result, and feeds that result back into the model's context.

The protocol itself is transport-agnostic: a server can run locally over stdio, or remotely over HTTP, which is what makes shared, hosted memory possible. A remote MCP server can serve every teammate and every client at once, since the protocol doesn't care whether the server is on your laptop or hosted infrastructure, only that it responds correctly to the standard calls.

Memory is one of the clearest use cases for the model context protocol, because memory is exactly the kind of capability that benefits from being shared across tools rather than reimplemented per client. A knowledge graph memory server is one of the protocol's own reference implementations for a reason: agents need to read and write durable facts, and MCP gives every client the same interface to do it.

Without a shared protocol, each client would need its own memory integration, and memory written by one tool wouldn't be visible to another. With MCP, a coding agent in Cursor, a chat session in Claude Desktop, and an automated agent running headless in CI can all point at the same remote MCP endpoint and read and write the same underlying memory.

Before MCP, teams built bespoke integrations: a plugin for one assistant, a different plugin for another, each maintained separately and each breaking independently when either side changed its API. That approach doesn't scale past a couple of tools and a couple of clients, and it puts the integration burden on every team building a tool, not just once on the protocol.

The model context protocol inverts that: build one server, and every current and future MCP client can use it without extra work on your end. That's a meaningful shift for anyone building infrastructure meant to serve more than one AI application, which by 2026 is most infrastructure worth building.

What made the protocol spread quickly wasn't just the specification, it was the reference implementations released alongside it. The official servers repository shipped working examples for common needs, file access, search, memory, from day one, which gave both server authors and client builders a concrete pattern to follow instead of interpreting a spec from scratch.

That head start compounded. Once a few major clients supported the protocol, building a new MCP server became worth it even for a narrow, internal use case, since the cost of building one server was now amortized across every current and future MCP-compatible client, not just whichever single tool a team happened to be using that quarter, and that math only gets better as more clients adopt the protocol.

It's worth being precise about what the protocol doesn't do. MCP doesn't decide what a server should return, rank results, or manage memory quality; it only standardizes how a client and server exchange calls and results. A poorly built MCP server behind a well-implemented client will still return poor results, since the protocol guarantees compatibility, not quality.

It also isn't a replacement for the underlying infrastructure a server needs. A memory-oriented server still needs somewhere to store and index memory, a database, a search index, a graph, MCP just standardizes how that infrastructure gets exposed to an AI client, not what the infrastructure itself looks like. Evaluating an MCP server on protocol compliance alone tells you it will connect; it tells you nothing about whether what comes back once it's connected is actually good.

stored exposes its entire memory layer as a remote MCP endpoint: one URL and a per-org API key is the whole integration. Claude Desktop, Cursor, and Claude Code all connect to the same endpoint and read and write the same shared memory graph, no SDK to install and no server for you to host or maintain.

Because the protocol handles discovery, tools like "search memory" and "write memory" show up automatically in any MCP client the moment it connects, and because the endpoint is remote and shared, every teammate's agents draw from and contribute to the same AI agent memory instead of each starting from a blank slate.

That also means switching or adding a client is a non-event. Pick up a new MCP-compatible tool next year, point it at the same endpoint and key, and it inherits the entire memory graph on the first connection, with no migration, no export step, and no separate setup beyond the same URL and credential every other client already uses, which is the whole point of standardizing on a protocol in the first place.