In brief
MCP stands for Model Context Protocol. It is an open protocol for connecting AI applications with tools and information sources. It describes how these components advertise their capabilities and exchange requests. This means that not every connection needs to use its own interface language from scratch. MCP itself is not a language model, database or search engine.
Which components work together?
An AI application acts as the host. Within it, a client maintains the connection to an MCP server. This server is software that offers various functions, such as document searches or data retrieval. A server can run locally or be accessible via a network. The word ‘server’ here does not necessarily refer to a separate physical computer.
Tools, resources and prompts
MCP distinguishes, amongst other things, between tools for executable functions, resources for available information and prompts for reusable instructions. The host determines how these capabilities are used within the application. A protocol governs the exchange; it does not determine which model provides the best answer or how your company should carry out a work process.
A practical example
An internal assistant must be able to search through manuals. You can set up an MCP server that offers a search function and returns only those documents to which the employee has access. A suitable AI application can discover and utilise that function. The underlying document archive remains in place; MCP acts as a link to that source, not a replacement for the archive.
What do you still need to sort out yourself?
You remain responsible for access rights, reliable tools and what the application is permitted to do with the results. A local MCP server can, in turn, access external systems. Connecting locally does not, therefore, automatically mean that all data remains local. For each connection, specify where processing takes place and what information the user and the model actually receive.
When is MCP suitable?
It can be useful when multiple suitable AI applications need to use the same sources or functions. For a single, simple connection, a standard API may suffice. Choose based on supported functions and management, not because the protocol is popular. Also check the supported protocol versions before considering a connection to be interchangeable.
From concept to practice
How AITJE applies this
When integrating systems, we assess whether MCP is supported by the chosen AI application and whether it offers benefits for the required tools. If suitable, a source or function can be made available via an MCP server. We continue to manage the permissions and data flows for each function. MCP is therefore a potential integration option for bespoke solutions, not a guarantee that every existing integration is automatically interchangeable.
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