MCP enables retrieval workflows with embeddings by giving AI models a structured way to call external vector search tools whenever they need additional context. Instead of embedding all retrieval logic directly inside prompts or custom code, developers define MCP tools that expose functions such as generating embeddings, inserting them into Milvus, or running similarity searches. The model uses the protocol to discover these tools and invoke them automatically when reasoning indicates that retrieval is needed.
The retrieval process typically begins when a model identifies a gap between its internal knowledge and what the user is asking. Instead of hallucinating an answer, the model can call a tool—such as one that queries Milvus—by passing a query embedding and requesting the nearest neighbors. MCP ensures that this call uses a precise schema, so the model knows what parameters are required and how to interpret the response. After receiving the retrieved documents or metadata, the model incorporates them into its response, forming a retrieval-augmented workflow.
By separating reasoning from retrieval infrastructure, MCP helps maintain clean and maintainable pipelines. Models do not need to understand Milvus internals such as index types, partitioning, or consistency levels. The MCP server handles those details, while the model interacts only with well-defined tools. This makes it easier to upgrade embeddings, improve index performance, or add hybrid search support without modifying the model logic. MCP’s structured communication approach makes retrieval workflows consistent across environments, whether running locally or in production.