Developers register new tools inside an MCP server by defining a tool specification that includes the tool name, a description, and a structured JSON schema for its input and output. This specification tells AI systems exactly what arguments the tool expects and what data it will return. Once the schema is defined, the developer implements the tool’s backend logic—such as running a function, querying a database, or manipulating a file—and attaches the implementation to the MCP server’s tool registry. The server then exposes the tool to any connected MCP client.
To register a tool, developers typically create a configuration file or code module listing each tool. The schema might define properties such as required fields, allowed value ranges, or data types (for example, arrays of floats for embeddings). The MCP server broadcasts this information to the model during initialization, allowing the model to understand how to call the tool safely. The model can then request a tool invocation by sending properly formatted arguments matching the schema.
For vector database use cases, developers often wrap Milvus operations into MCP tools. A tool might expose Milvus insert operations, search queries, index management actions, or hybrid queries that combine filters and vector similarity. Registering such tools allows an AI model to run vector search steps directly through MCP. This keeps vector operations encapsulated, testable, and consistent across environments while enabling the model to decide when vector retrieval is needed during reasoning.