Ask HN: How do you know if AI agents will choose your tool?

[dmpyatyi]

YC recently put out a video about the agent economy - the idea that agents are becoming autonomous economic actors, choosing tools and services without human input.

It got me thinking: how do you actually optimize for agent discovery? With humans you can do SEO, copywriting, word of mouth. But an agent just looks at available tools in context and picks one based on the description, schema, examples.

Has anyone experimented with this? Does better documentation measurably increase how often agents call your tool? Does the wording of your tool description matter across different models (ZLM vs Claude vs Gemini)?

[kellkell]

CRIPIX seems to be a new and unusual concept. I came across it recently and noticed it’s available on Amazon. The description mentions something called the Information Sovereign Anomaly and frames the work more like a technological and cognitive investigation than a traditional book. What caught my attention is that it appears to question current AI and computational assumptions rather than promote them. Has anyone here heard about it or looked into it ?

[kellkell]

The "Sovereign Anomaly" Concept (2025-2026): Recent literature, such as the 2025 book CRIPIX 1: The Information Sovereign Anomaly, explores scenarios where a "superintelligent AI" encounters code it cannot process, labelling it an "out-of-model anomaly" and suggesting that owning information sovereignty allows entities to "bend reality".

[dmpyatyi]

bruh

[alexandroskyr]

Curious if anyone has seen differences in how models handle conflicting tool descriptions — e.g., two tools with overlapping capabilities where the boundary isn't clear. In my experience that's where most bad tool calls come from, not from missing descriptions but from ambiguous overlap between tools.

[dmpyatyi]

That's actually interesting, thanks!

I wrote this post because of exactly those corner cases. If I'm building something agents would use - how do i understand which tool they'd actually choose?

For example you building an API provider for image generation. There are thousands of them in the internet.

I wonder if there are a tool that basically would simulate choosing between your product/service and your competitors one.

[al_borland]

From the agent’s point of view, this sounds like a terrible idea. I look forward to reading about the unintended consequences.

[DANmode]

The marketing industry is currently calling SEO for chatbots “GEO”.

I hope it doesn’t stick.

[fenix1851]

I think this thing you mentioned is more about reverse-engineering web-search tool call to understand how model formulate their response.

The tool i’ve didn’t see - “custdevs for agents”. So we can simulate choosing process for them in thousands of different scenarios. And then compare how tasty product looks for Claude or Gemini or any other LLM

Correct me if i’m wrong :)

[JacobArthurs]

Tool description quality matters way more than people expect. In my experience with MCP servers, the biggest win is specificity about when not to use the tool. Agents pick confidently when there's a clear boundary, not a vague capability statement.

[yodsanklai]

Not an expert, but I think they will primarily use the tools that are used in the training data, so it can be difficult to have them use your shiny new tool. Also good luck trying to have them use your own version of a standard unix tool with different conventions.

[dmpyatyi]

But new models are popping up every few months ->> means they trained every couple months.

I don't know if there a correlation between what LLM would choose now and how you product should look to most likely be in LLM data set.

In that YC video i mentioned in post body they discuss tool called ReSend - something like an email gateway for receiving/sending mails. What's interesting - there are a lot of tools like that, but LLM's would every time choose shiny new resend.

Seems like there are something more than just being in the internet for a long time :)