MCP connects models to external data sources by defining a structured interface for tools, resources, and prompts that a model can interact with during execution. Instead of giving models arbitrary system access, MCP exposes a controlled set of capabilities that are explicitly declared and described by the server. This means the model knows exactly what data sources are available (such as files, APIs, or databases), what arguments each tool accepts, and what form the output will take. The model can then decide when and how to call these tools based on the protocol’s instructions.
When an AI system receives a query that requires external information, the MCP client sends a tool invocation request to the MCP server. The server performs the operation—such as reading a document, running a query, or fetching an asset—and returns the structured result to the model. This interaction pattern ensures that models remain deterministic from the perspective of tool usage: they do not execute arbitrary code or access unknown resources. Instead, they operate within a curated environment where every external data source is explicitly allowed and described.
Vector database workflows benefit from this structure because models often need to retrieve embeddings, document chunks, or metadata to enrich their responses. MCP allows the model to call a Milvus-based search tool just like calling any other tool. The model can send a query embedding, request nearest neighbors, and receive structured search results. Because the interface is standardized, developers can update the underlying Milvus indexes without changing how the AI system performs lookups, which reduces maintenance overhead and keeps the retrieval process consistent across deployments.