I don't know, to me it seems like the LLM cli tools are the current pinnacle. All the LLM companies are throwing a ton of shit at the wall to see what else they can get to stick.
My use case was for using it as an advanced search tool rather than for creating tickets or documentation. Considering how poor the Confluence search function is, the results from Confluence via an MCP-powered search are remarkably good. I was able to solve one or two obscure, company-specific issues purely by using the MCP search, and I'm convinced that finding these pages would have been almost impossible without it.
MCP servers were also created at a time where ai and llms were less developed and capable in many ways.
It always seemed weird we'd want to post train on MCP servers when I'm sure we have a lot of data with using cli and shell commands to improve tool calling.
But even those are not better for agent use than the human cli counterpart.
That said the core argument for MCP servers is providing an LLM a guard-railed API around some enterprise service. A gmail integration is a great example. Without MCP, you need a VM as scratch space, some way to refresh OAuth, and some way to prevent your LLM from doing insane things like deleting half of your emails. An MCP server built by trusted providers solves all of these problems.
But that's not what happened.
Developers and Anthropic got coked up about the whole thing and extended the concept to nuts and bolts. I always found the example servers useless and hilarious.[0] Unbelievably, they're still maintained.
[0]: https://github.com/modelcontextprotocol/servers/tree/main/sr...