Deploying an MCP server in existing infrastructure typically involves integrating it into the environment where external tools already operate, whether that’s a container cluster, a virtual machine, or a local application runtime. The first step is selecting a runtime environment that has access to the systems the MCP server will wrap—such as filesystems, REST APIs, or vector databases like Milvus. The server process can run alongside these systems to minimize latency and simplify authentication.
Once the server is deployed, developers define tools using JSON Schemas and implement backend logic. For Milvus integration, tools might include “insert_embeddings,” “vector_search,” or “create_index.” These tools call the underlying services through existing SDKs or internal APIs. The MCP server handles input validation, execution flow, error handling, and response formatting. Because MCP is transport-agnostic, the server can run over standard communication channels without requiring specialized network components.
The final deployment step is configuring security, monitoring, and operational controls. Authentication may be handled through OS-level permissions, network controls, or API gateways. Logging and metrics can be integrated into the server so teams can track tool usage, detect failures, or monitor performance. Because MCP introduces a structured control layer around retrieval and indexing operations, deploying it into an existing infrastructure typically enhances—not disrupts—existing workflows. Teams can even roll it out gradually by exposing a single tool first and expanding the toolset over time.