| >How does the AI bypass the MCP layer to make the request It doesn't. I don't know why the other commenters are pretending this step does not happen. There is a prompt that basically tells the LLM to use the generated manifest/configuration files. The LLM still has to not hallucinate in order to properly call the tools with JRPC and properly follow MCP protocol. It then also has to make sense of the structured prompts that define the tools in the MCP manifest/configuration file. It's system prompts all the way down. Here's a good read of some the underlying/supporting concepts: https://huggingface.co/docs/hugs/en/guides/function-calling Why this fact is seemingly being lost in this thread, I have no idea, but I don't have anything nice to say about it so I won't :). Other than we're all clearly quite screwed, of course. MCP is to make things standard for humans, with expected formats. The LLM's really couldn't give a shit and don't have anything super special about how the interact with MCP configuration files or the protocol (other than some additional fine-tuning, again, to make it less likely to get the wrong output). |
No, there isn't. The model doesn't see any difference between MCP-supplied tools, tools built in to the toolchain, and tools supplied by any other method. The prompt simply provides tool names, arguments, and response types to the model. The toolchain, a conventional deterministic program, reads the model response, finds things that meet the models defined format for tool calls, parses out the call names and arguments, looks up in its own internal list of tools to find matching names and see if they are internal, MCP supplied, or other tools, and routes the calls appropriately, gathers responses, does any validation it is designed to do, then mals the validated results into where the model's prompt template specifies tool results should go, and calls the model again with an new message appended to the previous conversation context containing the tool results.