* What algorithm does tool_search use?

* Can tool_search search subcommands only?

* What's your argument for a harness having a hacked in bash wrapper nestled into the MCP to handle composition being a better idea than just using a CLI?

* Shell + CLI gives you basically infinite workflow possibilities via composition. Given the prior point, perhaps you could get a lot of that with hacked-in MCP composition, but given the training data, I'll take an agent's ability to write bash scripts over their ability to compose MCPs by far.

tool --help

tool subcommand1 --help

tool subcommand2 --help

man tool | grep "thing I care about"

As for stateful behavior, say you have the google docs or email mcp. You want to search org-wide for docs or emails that match some filter, make it a data set, then do analysis. To do this with MCP, the model has to write the files manually after reading however many KB of input from the MCP. With a cli it's just "tool >> starting_data_set.csv"

If an enterprise already has internal tooling with authn/z, there's no reason to overlay on top of that.

MCPs main value is as a structured description of an agent-usable subset of an API surface with community traction, so you can expect it to exist, be more relevant than the OpenAPI docs.

MCPs needs to be wrapped to be composed.

MCPs needs to implement stateful behavior, shell + cli gives it to you for free.

MCP isn't great, the main value of it is that it's got uptake, it's structured and it's "for agents." You can wrap/introspect MCP to do lots of neat things.

Besides the heavily oak covered hill country west of Austin it's pretty much the ugliest landscape in the country. I will admit the west Texas desert is less ugly than the desert of southern Arizona/eastern California, but north/east Texas is the flattest, least interesting part of the Mississippi basin (Nebraska/Kansas/Oklahoma are similarly meh but you don't have the insane humidity).

For example, Claude has a "turn evil in response to reinforced reward hacking" behavior which is a fairly uniquely Claude thing (as far as I've seen anyhow), and very likely the result of that attempt to imbue personhood.

Their whole "training the LLM to be a person" technique probably contributes to its pleasant conversational behavior, and making its refusals less annoying (GPT 5.2+ got obnoxiously aligned), and also a bit to its greater autonomy.

Overall they don't have any real moat, but they are more focused than their competition (and their marketing team is slaying).

If humans can prove that bespoke human code brings value, it'll stick around. I expect that the cases where this will be true will just gradually erode over time.

Whatever models suck at, we can pour money into making them do better. It's very cut and dry. The squirrely bit is how that contributes to "general intelligence" and whether the models are progressing towards overall autonomy due to our changes. That mostly matters for the AGI mouthbreathers though, people doing actual work just care that the models have improved.

Of course, that assumes a Bob with drive and agency. He could just as easily tell the AI to fix it without trying to stay in the loop.