The Model Context Protocol (MCP) is an open protocol that lets AI applications connect to databases, files and business tools in a standard way. What it is for, how businesses use it and what to watch out for.
MCP in short
MCP (Model Context Protocol) is an open protocol that lets AI applications connect to external systems — databases, file stores, CRMs, calendars or your own APIs — in a standard way. Anthropic announced it as open source in November 2024, and it was soon supported by other AI providers and developer tools. The common analogy: a USB-C port for AI applications.
Why it matters
Before MCP, every AI integration was written separately: one connector for one model, another for the next. With MCP you expose a system once as an “MCP server”, and every client that supports it — chat assistants, code editors, agents you build yourself — can use it through the same interface. That lowers integration cost and reduces lock-in to a single provider.
How it works
The architecture has three parts: the application the user interacts with (the host), the MCP client inside it, and an MCP server that exposes a system. A server can offer tools (for example “create order”), resources (a file or a record) and ready-made prompt templates. The model decides which tool to call and when; the application carries out the call within the user’s permissions.
Now an industry standard
In December 2025, MCP was contributed to the Agentic AI Foundation, set up under the Linux Foundation. Its founders include Anthropic, OpenAI and Block, with companies such as Google, Microsoft and AWS among its supporters. The move means the protocol will evolve under an open community rather than a single company.
How businesses can use it
An assistant for your support team that checks order status and opens return requests; an agent for sales that is connected to the CRM and drafts quotes; or a knowledge assistant that lets employees search internal documents. What they share is AI working with up-to-date data from your own systems.
Security and permissions
Giving an AI a tool means giving it authority. Apply least privilege on every MCP server; separate read from write operations, ask for user confirmation on critical actions, log every call, and remember that outside content (an email or a web page) can be processed by the model as if it were an instruction (prompt injection). If personal data is involved, build privacy obligations such as KVKK or GDPR into the design from the start.
Where to start
Pick a small, measurable use case, expose a single system over MCP with read-only access, try it with real users and measure the results. Widen permissions and scope step by step as it proves itself. This is the path we walk together in our AI integration service.
