What is an MCP server?

What is the Model Context Protocol?

MCP stands for the Model Context Protocol. It is an open standard that defines how an AI application talks to external tools and data. Before MCP, every team that wanted their assistant to read a database, call an API, or open a file wrote custom integration code for that one assistant. Swap the model or the app, and the work started over. MCP replaces that one-off glue with a shared contract: any AI app that speaks MCP can connect to any tool that speaks MCP.

The protocol has two sides. The client is the AI application, such as Claude or a coding agent. The server is the program that exposes a capability the AI can use. The client and server exchange messages in a fixed format, so the AI does not need to know the internals of each tool. It only needs to speak the protocol.

What is an MCP server, exactly?

An MCP server is the adapter on the tool side of that conversation. It advertises a set of capabilities and then handles requests for them. Servers typically expose three kinds of things:

• Tools are actions the agent can call, like "search the issue tracker" or "send a draft email." The agent decides when to call them and passes structured arguments.
• Resources are data the agent can read, like a file, a document, or a row from a database. They give the model context to reason over.
• Prompts are reusable templates the server offers so a user or agent can trigger a known workflow in one step.

Because these are described in a standard way, the AI can ask a server what it can do, get back a machine-readable list, and start using it without any hard-coded knowledge of that specific tool. That is the whole point: one protocol, many servers, any compatible model.

How do MCP servers work?

A typical exchange runs in a few steps. First, the AI application starts or connects to the MCP server and asks what it offers. The server returns its list of tools, resources, and prompts. When the model is working on a task and decides it needs one of those capabilities, it issues a call. The server runs the underlying work, whether that is a database query, an API request, or a file read, and returns a structured result. The model folds that result into its reasoning and continues.

Servers can run locally on your machine, for example a filesystem server the agent uses to read your project, or remotely behind an API for a shared service. The transport differs, but the contract is the same. This is what lets a single agent combine many servers at once: a code agent might use a filesystem server, a git server, and a memory server together in one session.

Examples of MCP servers

The ecosystem already covers a wide range of jobs. Common categories include:

• Filesystem and code servers that read and edit files in a project.
• Database and API servers that query internal systems on request.
• Search and web servers that fetch and summarize external content.
• Memory servers that store and recall context so the agent does not start from zero every session.

That last category is where a lot of real-world value shows up, because the biggest limitation of most agents is not intelligence. It is amnesia.

How do memory MCP servers fit in?

By default, most AI agents forget everything when a session ends. Close the window, open a new one tomorrow, and the agent has no memory of what you decided, what it learned, or how your project is structured. A memory MCP server solves this by giving the agent durable storage it reaches through the same protocol it already uses for every other tool.

Instead of keeping facts in the model context, which disappears, a memory server writes them to persistent storage and serves them back on the next session. Good memory servers store more than raw notes: they keep executable playbooks the agent can follow, verified claims it can trust, and compact context packs it can load in a single call. Because the server is model-agnostic, the same memory travels with you whether you switch from Claude to ChatGPT or from one coding agent to another.

Keynodex Memory is a model-agnostic MCP memory server built for exactly this: persistent memory, executable playbooks, verified claims, and one-call context packs that your AI agents share across sessions and tools.

How do I add an MCP server?

The exact steps depend on your AI app, but the shape is consistent. In Claude Code, you register a server with the claude mcp add command. A remote server takes a URL and authenticates in your browser. Connecting a hosted memory server, for example, looks like claude mcp add --transport http keynodex-memory https://memory.keynodex.com/mcp. A local server instead takes a launch command with its arguments and any environment variables it needs. Either way, Claude Code connects to the server, discovers the tools it exposes, and makes them available to the agent.

Other MCP clients follow the same idea: point the app at the server, let it enumerate the capabilities, and grant the agent access. Once connected, the server is just another set of tools the model can reach for when it needs them.