An MCP client calls external tools by following the protocol’s structured message flow: discovering available tools, selecting a tool, constructing a JSON-formatted argument payload that matches the tool’s schema, and sending a tool invocation request to the server. MCP clients do not execute the tool themselves; they request actions from the MCP server, which performs the operation and returns a structured response. This separation ensures that tool execution remains controlled and predictable, while the client focuses on reasoning and decision-making.
The typical workflow begins when the client queries the server for the list of available tools. Each tool includes a name, a description, and a JSON Schema defining inputs and outputs. When the model decides to use a tool, it constructs a payload that matches the schema exactly. The MCP client then sends a request using the standardized invocation format. The server validates inputs, executes backend logic—such as running a vector search or generating an embedding—and returns a JSON response containing the results or an error state.
In vector search workflows, an MCP client might call a tool such as “milvus_search” by providing an embedding vector and parameters like "top_k". Once the server executes the query in Milvus, it returns nearest neighbors in a structured format the model can immediately use. This makes vector retrieval a first-class capability inside AI reasoning rather than a custom system bolted on afterward. Because the entire process is schema-driven and predictable, developers can easily test, monitor, and refine retrieval pipelines without changing model code.