What is MCP (Model Context Protocol)?

MCP, the Model Context Protocol, is an open standard enabling AI applications to connect to external tools, data sources, and services through a common interface. Instead of every AI app having its own way to connect to a database, file store, or memory system, MCP defines a shared protocol that lets clients and servers with compatible protocol features and transports communicate.

A useful analogy is a shared connector standard: MCP reduces the need for a different integration interface in every AI application. Compatibility and permissions still matter. For memory, the benefit is that several supported clients can use the same storage service.

TL;DR: what is the Model Context Protocol?

MCP is a standardised, language-agnostic protocol for connecting AI applications to servers that expose tools, resources, and prompts. It reduces client-specific integration work by defining how they communicate. Each application still needs compatible protocol support, configuration, and a way to decide which capabilities to use.

Why does MCP exist?

Without a common integration protocol, connecting several assistants to documents, databases, and a memory store can require separate adapters. Those adapters may duplicate work and need maintenance as the applications or services change.

With separate adapters, the work can grow with the number of AI applications multiplied by the number of services. A shared protocol can reduce that duplication: applications implement client support, and services expose a common server interface. Each connection still needs compatible features, transport, and authentication.

How do MCP clients and servers work?

The AI application is the host. It manages MCP client components that connect to servers. Each connection has two sides:

• MCP client, a component within the host that maintains a connection to an MCP server and exchanges requests and context on the application's behalf.
• MCP server, a program that exposes capabilities such as tools, resources, and prompts. It may run locally or as a remote service.

Servers can expose three core kinds of capability:

• Tools, functions the application can invoke through a client, such as searching memory or creating a file.
• Resources, data the application can read and include in the model's context, such as documents or database records.
• Prompts, reusable templates that the server makes available to structure interactions with a model.

A client can discover the capabilities a server exposes, but the host controls which ones are available and when to use them. Permissions or approvals may apply. MCP itself does not store conversation history or decide what goes into a model request. An AI application may have its own history or memory, and an MCP service can supply additional context. For more on the distinction, see Why AI forgets conversations.

What does MCP unlock?

MCP lets compatible assistants query systems, edit files, and call APIs through a shared interface. It can also make context available from the same remote service to several clients, including supported setups in ChatGPT, Claude, Cursor, and Codex. For Vilix AI, the Gemini app is not currently a supported custom-MCP client; developer tools using Gemini models are a separate case.

A memory service can expose tools through MCP so that compatible, authenticated clients can save and request context from the same account. For example, a decision successfully saved from Claude can be requested in Cursor. The receiving client still has to make the request and use the returned information. We explore that workflow in Why cross-AI memory matters.

Why does MCP matter for memory specifically?

To reuse memory across tools, you need durable storage and a supported way for each tool to reach it. MCP standardises parts of that communication; the service and its clients handle storage, access, and how retrieved context is used.

A memory service can expose one MCP interface for several compatible applications, reducing the need for separate adapters. You still configure and authenticate each client, and each host controls which tools it invokes.

This is how Vilix AI supports cross-tool memory: connect each supported client to your same Vilix AI account, authenticate, and add the Vilix AI instructions or agent-loop calls. Before each response, call get_context with the latest user message and use the returned context to compose a reply. Call save_turn with the exact user message and composed reply, then return that same reply after the save succeeds. Reuse chat_id within the conversation and verify that retrieval and saving both succeed. Vilix AI stores the exchanges actually submitted to it; retrieval returns relevant available records within your plan's limits, with semantic search on eligible plans. You can export saved data and delete records from the active service; backup removal follows the privacy policy. The common server does not automatically share memory between different users' accounts.

Is MCP a model, a product, or a standard?

MCP is a standard: a specification for communication between clients and servers. It is not a model or a single company's product. Anyone can implement a compatible client or server.

Frequently asked questions

What does MCP stand for?

MCP stands for Model Context Protocol. It is an open standard for connecting AI applications to external tools, data, and context through a common interface.

Is MCP only for Claude?

No. MCP is provider-neutral. While it originated in the Anthropic ecosystem, it is an open protocol that a range of AI clients and tools can implement, which is the point of a standard.

What is the difference between an MCP client and an MCP server?

The AI application is the host. An MCP client is the component it uses to connect to a server. The server exposes capabilities, such as tools, resources, and prompts, that the host can use through that connection.

Does MCP give an AI memory?

Not by itself. MCP defines how applications communicate with external services. Persistent memory requires a service that stores and retrieves context, plus a compatible client configured to make those calls.

Why is MCP important for using multiple AI tools?

A remote MCP service can support several compatible clients through the same interface. With separate connections and authentication in each tool, it can provide a common place to save and request context. The protocol reduces integration work; it does not automatically transfer every conversation.