The minimal components of an MCP implementation are an MCP server, an MCP client, and a set of well-defined tools that expose capabilities through JSON Schema. The protocol is intentionally simple so developers can start small. The server runs the actual logic—such as filesystem access, API calls, or vector search—while the client (usually embedded in the model runtime) discovers available tools, sends requests, and interprets structured responses. With just these two pieces and a single tool definition, you have a functioning MCP environment.
The second essential component is the tool schema. MCP requires each tool to specify its expected inputs and outputs using JSON Schema. This prevents ambiguity and ensures the model always knows exactly what arguments to send. A minimal setup might define a tool with a simple structure like { "message": "string" }, but more realistic tools include typed arrays, numeric parameters, or nested objects. Schema validation is handled by the server, freeing the model from guessing about formats or error states.
In vector search setups, these minimal pieces are enough to wrap Milvus operations behind MCP tools. A developer might build a “vector_search” tool with a schema describing the embedding array, the top-k value, and the collection name. Even with only this tool, the model can perform semantic search simply by calling the MCP server. The minimal design allows teams to scale up over time—adding indexing tools, ingestion pipelines, or batch operations—without altering the core structure of the implementation.