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No. The Model Context Protocol (MCP) standardizes how an AI application connects to servers that provide tools; it does not, by itself, send those tools to a model or run the model’s tool-calling loop. The host application still supplies tool definitions to its chosen model API, receives any returned tool call, routes it through an MCP client, and returns the result to the conversation.
What MCP handles—and what it leaves to the host
MCP is an open standard for connecting AI applications to systems where data and tools live. An MCP server advertises tools, and an MCP client can discover and invoke them. The host application coordinates that protocol work with its model API; those are related jobs, but they are not the same interface.
| Responsibility | MCP client and protocol | Host and model API |
|---|---|---|
| Discover available tools | Lists tools advertised by an MCP server and retrieves their definitions. | Receives the definitions for inclusion in a model request. |
| Choose whether to call a tool | The protocol operations described here do not establish a universal model decision. | Sends definitions to a tool-calling model API, which may return a tool call. |
| Run the selected tool | Provides the protocol-level operation to invoke a named tool with arguments. | Routes the model’s returned tool name and arguments to the MCP client. |
| Continue the conversation | Returns the tool result. | Adds that result to the conversation and continues the model interaction as needed. |
The MCP TypeScript SDK’s Build your first client guide shows this separation: the client calls listTools(), the host gives the resulting definitions to a tool-calling LLM API, and then the host passes a returned call to callTool(). In the guide’s words, “when the model returns a tool call, pass its name and arguments to callTool unchanged and append result.content as the tool result.”
How the MCP tool-calling loop works
- Connect: The host uses an MCP client to connect to a server.
- Discover: The client calls
listTools()and obtains tool names, descriptions, and input schemas. - Offer tools to the model: The host includes those definitions in a request to its chosen model API, using that API’s tool-calling interface.
- Route a returned call: If the model returns a tool call, the host forwards its name and arguments to the MCP client’s
callTool()operation. - Return the result: The client returns the tool result; the host adds it to the conversation and continues the interaction according to the application’s design.
The key architectural point is that MCP supplies the connection and protocol-level tool operations. The host supplies the model-facing definitions and orchestrates the round trip. A tool being available through MCP does not mean the model has automatically seen it or that MCP made a model request.
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Why “MCP client emits a function call” can be misleading
In this flow, the model returns the tool call in the format expected by the model API. The host then invokes the MCP client with the returned tool name and arguments. The client emits or sends the corresponding protocol operation to the MCP server; it is not necessarily generating the model’s function-call object.
So “function calling” describes the model-facing mechanism, while MCP describes how the application discovers and invokes tools across the client-server boundary. The host connects the two. The MCP C# SDK API reference similarly describes McpClientTool as enabling integration with AI models that support function-calling capabilities.
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What varies between implementations
The described sequence is documented in the TypeScript SDK guide; it should not be read as a guarantee that every host, provider, or SDK exposes identical methods or function-call formats. The host’s chosen model API determines how tool definitions and returned calls are represented, and the host remains responsible for translating or routing them appropriately.
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