I have a tool wrapper that captures the output of anything and allows the LLM to query it later, to save on tokens. It “smartly” truncates the output (basically like Node’s util.inspect) and allows the LLM to expand truncated content.

It basically is called like “capture some-cli” and it… captures the CLI output, outputting a subset of it + a handle to continue querying.

This for me solves the danger of a tool returning tons of content.

It seems like OP needs to provide a solution to hiding the credentials from the model in order to suggest CLI-mode only, and also a solution to the problem of agents without shell access.

You know you are getting old when Acronym's change on you.

I’m not sure I agree that the frontier just know the apis right now, in my experience trying this there’s still a lot of faffing around trying to figure out the right parameters happily burning tokens and bloating context. Also the cost effective models to use in production for real agentic enterprise work absolutely still need the extra help and will do for at least the next 6 months.

>Recently, a Vercel engineer called on harnesses to send the programming language the client prefers

oh would you look at that, Vercel suggesting to abuse how standard headers have been used for decades so it can send Accept-Language: rust because it's too lazy to ask for standardising an X-Prefers-Lang or anything else, and Shopify is here to shit on the internet too. Great.

Well, SaaS don't do CLIs for extension APIs.

Plus the performance issues to restarting processes all the time.

There are numerous applications that you don't need and don't want to give shell access to an llm.

i feel like MCP was bad, but people are saying recent improvements have made it worthwhile now? i.e. stateless http

There's probably still value (if you want to call it that) in it as a proxy, both to bypass IP address rate limits and to add necessarily credentials.

There's also another aspect Quite a few API providers provide automatic renewal for MCP server registrations, but not for personal access tokens. This may be less relevant when models just drive the user's browser.

It's not just the agent understanding the API, it's locking down the access they have. If I want to give access to an internal service in specific ways that the API doesn't lock down then an MCP that offers very specific queries, with protective controls and transformations in place is very useful.

MCPs are indeed useless, they're very limited in functionality and frequently struggle with large requests or get wedged in bad states.

There is no reason not to use the native API directly.

This doesnt match my experience. Yesterday, I was using Microsoft's Power BI Authoring MCP to make a semantic model from some SQL or CSV files. It was magical.

Microsoft has defined how to do that in the MCP. It's trivial to add the MCP to the machine and reliable in execution.

The alternative would be the model having to get the documentation directly from their documentation website, it sounds like. If this was the case, then MS would likely have great docs and probably support that markdown header... but everything hinges on finding a specific web page on the internet? Seems worse in every way than MCP to me.

MCP for agents never made sense, especially when the tokens they consume a significant amount of tokens on a single request for a basic action, and sustained usage blows up you token costs.

The spec was poorly designed to begin with. Even saw some folks here thinking it was a good idea to enable MCP directly on a production database for what? Risking exfiltration of sensitive data for bad AI agents.

Given the increased security capabilities of these new models (Mythos, Astra, K3), it sounds like MCP would not be able to justify on making sense from a security perspective and would be a very bad idea to use anyway.

So no thanks and no deal.

I’m not sure about some of this — I still think there is some value to MCP as a gateway to private resources when API access doesn’t exist.

But please don’t try to redefine the Accept-Language header. These things are well defined for a reason and redefining things in the fly for LLMs is how we got into the current mess. For all of the cruft that W3C has, I think that working with standards committees could help the AI vendors here.

MCPs are winning because within the ChatGPT and Claude apps, there are Plugin stores. These plugins are one-click installation MCP servers, with support for authentication. This is what business users are using.

> Recently, a Vercel engineer called on harnesses to send the programming language the client prefers, so documentation sites can serve more specific examples. For example, adding Python could prioritize docs for the Python SDK instead of sending something generic.

I know this is pedantic, but IMO that should just be in the URL if the resource is going to be totally different. Accept-Language is already a bit weird for the same reason in my mind, but I think the intention behind it is the resource itself is attempting to communicate the exact same resource. Obviously, two different languages from two different cultures are going to have different interpretations of the same direct translation, but the intention is the service has at least tried to avoid that as much as possible.

Adding programming language into that same concept just makes it seems like you're serving both /docs/typescript/vx/... and /docs/python/vx/... from /docs/vx/... despite them (in theory) having many more differences in between implementations/context than that would imply.

Agents should read sitemaps. Does anyone's harness do specifically that when looking up documentation?