MCP routes embedding requests to external engines by exposing embedding-generation functions as callable tools on the MCP server. When the model needs to convert text, images, or other inputs into embedding vectors, it calls the corresponding MCP tool with properly structured inputs. The tool then delegates the operation to an external embedding engine, which may run on local hardware, remote services, or specialized GPU nodes. The server collects the result—a vector or batch of vectors—and returns it to the model in a standardized JSON format.
This routing mechanism allows embedding generation to be decoupled from the model itself. Instead of embedding everything internally, the model triggers external embedding engines as-needed, allowing developers to use specialized or updated models without retraining or modifying the AI system’s core behavior. MCP ensures that the model knows the expected input shape and output format through its schema definitions, so embedding requests remain predictable. This is particularly helpful when the embedding engine is upgraded or replaced, because the MCP tool interface can remain constant even as internal logic changes.
For Milvus-based retrieval pipelines, routing embeddings through MCP tools ensures that generated vectors always match the dimensionality and format expected by the database. Developers can enforce checks inside the tool—for example, validating vector length before inserting into Milvus—to catch errors early. MCP also makes it possible to route requests across multiple embedding engines if needed, such as choosing between text or image embedding models based on context. This creates a flexible and stable architecture for retrieval-centered applications, with MCP serving as the coordination layer for embedding generation.