MCP handles large embedding batches efficiently by using structured, streaming-friendly JSON messages and delegating heavy computation to the MCP server rather than the client. The protocol does not require the client to perform bulk operations internally; instead, the client sends a request describing the batch and lets the server carry out the work. This keeps communication efficient because the server can perform batching, parallelization, or hardware-accelerated computation without requiring the model to handle those details. The model only needs to produce embeddings or provide instructions, while the server manages scale.
Developers may also define MCP tools that explicitly accept batch embeddings as arrays rather than single vectors. This reduces round-trip overhead by allowing the client to send one large structured request instead of many smaller ones. The server can then handle these batches in ways optimized for the infrastructure: using GPU acceleration, pipelining requests, or applying concurrency models appropriate for the system. MCP’s structured communication format makes it clear how many vectors are being sent, what their dimensions are, and how the results will be returned.
For Milvus workflows, batching is especially important because inserting or querying large numbers of vectors requires coordinated operations that benefit from batching. MCP tools can be designed to accept hundreds or thousands of vectors in a single call, allowing the server to insert them into Milvus efficiently or run batch similarity searches. By letting the MCP server optimize how these batches are handled, the model avoids unnecessary overhead and maintains consistent throughput. This also allows developers to enforce safe limits or caching strategies without modifying model logic.