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    Model Context Protocol (MCP)

    The Model Context Protocol (MCP) is an open protocol for connecting AI applications to external data and tools. It specifies how supported capabilities are exposed and used. MCP is neither an AI model nor an automatic security or quality guarantee.

    Model Context Protocol (MCP) explained

    An AI application needs product information from another system. It requires a connection, understandable functions and appropriate permissions. MCP can standardise the exchange between the application and a connected service. It does not independently determine which data is accurate or approved for use.

    A typical setup includes a host application, a client and a server exposing selected capabilities. These may include readable resources, tools and reusable prompt templates. Actual availability depends on both sides and the supported protocol version.

    A connection does not make every task work automatically. Check required fields, error reporting and permitted actions. Searching product data differs from changing a product catalogue.

    MCP does not replace permission enforcement in the connected system. A tool described as read-only still needs to be implemented and constrained accordingly. Tool descriptions are hints; their claims alone do not prevent unwanted changes. What matters in practice is a tested connection with clear responsibilities.

    Examples

    Hypothetical application

    An editorial assistant accesses approved product information through an MCP server. It can retrieve facts and prepare drafts. This access cannot modify product data. The team checks that correct versions are used and missing information remains visible.

    Key Points

    • MCP connects applications, data and tools through a common protocol.
    • Availability depends on implementation and supported capabilities.
    • Distinguish connectivity from editorial approval.
    • Enforce permissions technically rather than merely describing them.

    Practical application

    Define the task and permitted data access first. Assess whether available MCP capabilities fit. Test correct results, missing permissions, outdated data and failure conditions before using the connection in a production workflow.

    Useful measures

    Task-suitable results

    Whether the connection provides the correct information for the agreed task.

    Permission enforcement

    Whether forbidden access and actions are rejected in defined tests.

    Integration effort

    Setup, maintenance and error handling throughout use.

    Common mistakes

    • Describing MCP as an autonomous agent or model training.
    • Treating a connected system as a blanket-approved data source.
    • Confusing tool hints with enforced permissions.

    Sources and context

    Frequently Asked Questions about Model Context Protocol (MCP)

    MCP is a protocol for a particular form of integration. An MCP server may use existing APIs or other data access methods. It does not replace their business rules or permissions.

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