The Model Context Protocol (MCP) is an open standard that connects AI models to Tools, data sources, and APIs through a single, unified interface. This overview explains its structure, building blocks, and communication paths — and identifies where security questions arise along the way.
Until now, every connection between an AI model and a third-party system meant custom code: Tool schemas, functions, and their maintenance, built anew for each service. The Model Context Protocol (MCP), introduced and opened up by Anthropic in 2024, addresses exactly this and standardizes how models such as Claude connect to tools, data, and interfaces. Instead of building a dedicated connection for each service, the application communicates through a single, unified interface — an approach often described in practice as “USB-C for AI integrations”. The following overview explains the architecture, the three primitives, and the transport-agnostic communication. It places MCP within the agentic stack and shows where security-relevant questions arise.
MCP is an open standard and a communication layer that provides an AI model with context and tools without requiring custom integration code for each service. Instead of individual connections, the model connects to Tools, data sources, and APIs through a single, unified interface. Anthropic introduced and opened up the protocol in 2024.
MCP has two central building blocks. The MCP-Client is the access point; it lives in the host application, connects it to an MCP-Server, and calls that server's functions. The MCP-Server encapsulates an external service and exposes its functionality in a standardized way — a client can connect to any number of servers.
A server provides three kinds of building blocks: Tools perform actions, such as an API call; Resources deliver readable data as context; Prompts are predefined instructions. They differ in how they are controlled: Tools are model-controlled, Resources application-controlled, and Prompts user-controlled.
MCP is transport-agnostic: client and server can communicate over stdio (on the same machine, via standard input/output), over HTTP (across the network, for remote servers), or over WebSockets; the standard does not prescribe any single channel. Once the connection is established, both sides exchange clearly defined messages — including listing the available Tools and calling a Tool, each as a request and as a result.
At its core, MCP shifts the load: defining and executing Tools moves out of your own application and into specialized MCP-Servers. Providers increasingly publish their own servers with ready-made Tools, so that less custom integration code and less maintenance are required. In doing so, MCP does not replace the concept of “Tool Use” but complements it: Tool Use describes the what, while MCP governs who defines and operates the Tools.
Within the agentic stack, MCP is the standardized connection layer between an AI agent and the surrounding landscape of Tools, data, and APIs. Precisely because a model triggers actions through it and accesses remote servers as well as external data, questions arise about permissions, trust in third-party servers, data flows, and control over model-driven actions. VamiSec supports these topics with assessments, penetration tests, and consulting; a deeper look at MCP security follows in the subsequent articles.
The content on this page is based on the following publicly available guides and studies.
Course, published on March 30, 2026; the technical basis of the VamiSec webinar.
VamiSec's preparation and practical contextualization of the protocol fundamentals.
Official, versioned protocol specification (client/server, primitives, transport).
Reference SDK; generates Tool schemas from decorator, type hints, and docstring.
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