MCP coordinates multiple retrieval tools by giving the model a structured way to discover available tools and call them sequentially or conditionally based on the reasoning steps it produces. Each retrieval capability—such as embedding generation, vector search, metadata lookup, re-ranking, or text extraction—is represented as an individual tool. The model sees all of these tools during capability discovery and can choose the correct sequence of actions based on its understanding of the task.
This design allows for flexible multi-step workflows. A model might embed a query, run a Milvus search, request metadata for the top results, and then run a re-ranking tool before generating an answer. MCP ensures that each tool returns structured results the model can immediately use as input for the next step, eliminating the need for custom routing logic. The protocol’s consistency makes it easy for the model to chain operations without confusion.
In vector-centric retrieval workflows, this coordination becomes especially powerful. For example, the model can use “embed_text” → “milvus_search” → “fetch_document” as a pipeline. Developers may add optional tools like “summarize_document” or “apply_filters” to enrich the workflow further. Because all tools follow the same MCP structure, the model can dynamically adapt, combining retrieval steps as needed without developer intervention. MCP essentially turns retrieval services into modular building blocks the model can orchestrate intelligently.