MCP improves interoperability across retrieval services by acting as a standard interface that AI models use to interact with external systems. Instead of every backend exposing its own SDK, RPC format, or custom API, MCP wraps each retrieval capability behind a unified schema and message flow. The model doesn’t need to know which system it’s interacting with; it only needs to understand the tool’s schema. This standardization removes friction in environments where multiple retrieval systems coexist.
A major benefit is that MCP treats each retrieval operation as a first-class tool, whether it involves vector search, keyword lookup, metadata filtering, or document extraction. Since all tools follow the same JSON Schema pattern, the model can call any retrieval service using identical logic. This makes the retrieval layer more modular. A tool can be removed, replaced, or modified without forcing changes in model logic. The model simply chooses actions based on available tool descriptions.
For vector databases like Milvus, interoperability means the model can combine vector search with other retrieval methods cleanly. A developer might expose Milvus queries alongside tools for re-ranking, metadata joins, or filtering. MCP ensures these operations speak a common language, enabling hybrid retrieval systems where vector similarity, metadata filtering, and content extraction all coexist smoothly. This interoperability is essential for building robust retrieval-augmented generation pipelines that rely on more than one source of truth.