I can't agree more, downloading OpenAPI doc for an API and parse it is more than enough to implement the core of MCP. But sadly the buzzword completely took of and for instance all participants to my trainings will ask for MCP, systematically.

The LLM doesn't intervene much actually, it just tells what tool to call. It's your MCP implementation that does the heavy lifting. So yeah you can always shove a key somewhere in your app context and pass it to the tool call. But I think the point of other comments is that the MCP protocol is kinda clueless about how to standardize that within the protocol itself.

My understanding is that it can upgrade to an SSE connection so a persistent stream. Also for interprocess communication you usually prefer a persistent connection. All that to reduce communication overheads. The rationale also is that an AI agent may trigger more fine-grained calls than a normal program or a UI, as it needs to collect information to observe the situation and decide next move (lot more get requests than usual for instance).

Just use a normal static analysis tool and shove the result to an LLM. I believe Anthropic properly figured that agents are the key, in addition to models, contrary to OpenAI that is run by a psycho that only believes in training the bigger model.