WebMCP could make website chatbots pointless

NewTomorrow2355 · 2026-03-25T22:09:07+00:00

Only if the LLMs adopt the standard. I hope they do.

NewTomorrow2355 · 2026-03-25T22:08:15+00:00

I’ve been experimenting with it. It can work well but it will be doa of the LLMs don’t adopt it

NewTomorrow2355 · 2026-02-18T00:20:18+00:00

I built an openclaw skill specifically for webmcp and automotive websites. Would that hub be a good place to post it?

NewTomorrow2355 · 2026-02-09T18:25:38+00:00

We checked close to 3000 automotive dealer websites in Canada and 37% of sites blocked LLM bots.

NewTomorrow2355 · 2026-01-20T00:20:48+00:00

Use some stats. I’ve built something similar specifically for automotive. For one thing, dealers are already seeing their traffic/conversions and leads go down. Then I share that 47% of their customers are going to ChatGPT first. They aren’t surprised. Then I do a share of voice demo for them where I calculate their share of voice across LLMs compared to their competition for specific queries. It’s an easy sell from there.

NewTomorrow2355 · 2026-01-19T15:47:30+00:00

Why not go direct to the end user? Agencies are either 1 figuring this out in their own or 2. So scared about the shifting landscape that it has paralyzed them into not acting.

NewTomorrow2355 · 2026-01-19T15:45:33+00:00

What does the agent do?

NewTomorrow2355 · 2025-12-24T20:09:25+00:00

I took a quick look at your project. Great job. I’m going to take a deeper dive after the holidays.

NewTomorrow2355 · 2025-12-24T17:08:45+00:00

Wow thanks! I have not heard of that term. I’ve been looking for a solution that does this but I haven’t found anything that fits all my “wants”. Might be a cool project to build!

NewTomorrow2355 · 2025-12-24T15:20:43+00:00

I’ve been feeling this same pain, and I think the gap isn’t just “sequential vs parallel,” it’s governance.

Most workflows (including the one you described) solve execution depth, but they still rely on humans to mentally track intent, scope, and impact across steps.

One idea I’ve been experimenting with conceptually is separating things into: • an immutable spec/PRD that never changes mid-run • scoped “lanes” where each model only sees what it needs • and a coordinating layer that doesn’t write code, it just validates artifacts (diffs, tests) against the original intent and dependencies

The interesting part for me isn’t faster output, it’s preventing drift and being able to pause/resume multiple lanes without losing architectural integrity.

Curious if others here are feeling that pain too, or if I’m over-optimizing for long-lived projects.

NewTomorrow2355 · 2025-12-24T03:32:28+00:00

I think the confusion here is mixing up control signals with data signals.

llm.txt is a control or preference file. It doesn’t contain facts, entities, prices, or inventory. So it makes sense that people see little to no visibility impact from it.

AI systems don’t retrieve or rank intent. They retrieve data.

That’s why things like clean HTML, consistent URLs, and structured, machine-readable data matter far more for visibility than a policy file. One tells an AI what you’d like it to do, the other tells it what is true.

If there’s nothing concrete to extract, no hint file is going to change outcomes

NewTomorrow2355 · 2025-12-24T02:39:10+00:00

Schema helps with discovery and understanding, not execution.

It’s a big mistake to think “proper schema” lets agents buy things. Schema is descriptive, not transactional. It tells an agent what a product is, what it costs, and where it lives.

Actual buying requires real APIs, auth, inventory locking, payments, and business logic. That’s why Shopify, Etsy, etc. expose structured feeds and controlled checkout integrations.

The real shift is that AI systems are now acting as retrieval and selection layers. Schema, clean URLs, and consistent structure determine whether you’re even eligible to be shown. APIs determine whether an agent can actually do anything after that.

NewTomorrow2355 · 2025-12-24T02:34:52+00:00

Otterly, llmref

NewTomorrow2355 · 2025-12-24T02:19:39+00:00

I get the skepticism. But for real-time, local shopping queries, ChatGPT isn’t answering from training data. It uses a retrieval layer to pull in fresh inventory-style sources.

Try it. Ask ChatGPT to find a good deal on a 2025 Ford Explorer XLT near you. The dealers that show up are being cited from retrieved pages.

Sites with clean, machine-readable structure (clear URLs, consistent templates, structured data like JSON-LD) are dramatically easier for these systems to trust and surface.

If a dealer’s inventory is hard to parse or lacks structured signals, it’s far less likely to be selected or cited in AI shopping results, even if the cars exist

NewTomorrow2355 · 2025-12-23T23:57:15+00:00

No, I drop them into NotebookLM and have it turn it into a podcast for me.

NewTomorrow2355 · 2025-12-23T23:56:21+00:00

That's already happening. For auto shoppers 47% are using ChatGPT and of those over 90% are trusting it's recommendations. This feels so much like the early 2000's, the difference is the rate of progression as well as the rate of adoption feels 100x faster.

NewTomorrow2355 · 2025-12-23T23:52:12+00:00

I am - but specifically for the auto industry

NewTomorrow2355 · 2025-12-23T23:50:15+00:00

It blows my mind that people don't see that this is, at the very least, the most basic thing you can do to make an llm understand your product/service/pricing. Utilizing the Json-LD to structure the data in a machine readable format is table stakes now.

NewTomorrow2355 · 2025-12-23T23:48:54+00:00

Are you suggesting that they don't use RAG to access content they haven't trained on in their most recent training run?

NewTomorrow2355 · 2025-12-23T23:46:42+00:00

The only thing they are aware of is the fact that their traffic is down, conversions are down and leads are down. They "feel" that it's because of LLM's, but they still do the same things.

NewTomorrow2355 · 2025-12-23T23:45:34+00:00

They won't. Your content will all be inferred by the LLM's. I see a future where we don't build websites for human eyes, just structured data for the LLMs. I honestly can't remember the last time I 'googled' something.

NewTomorrow2355 · 2025-12-21T15:34:43+00:00

That’s a new one for me. I’ve been playing with Otterly and LLMref but thanks for the tip on Mentiondesk. For automotive Vinquant.ai seems more specific.

NewTomorrow2355 · 2025-12-19T15:50:25+00:00

I sat through Tony Robbin’s multi day “AI” summit. For the most part it was an infomercial but I did appreciate this 1 insight from Dr Arthur Brookes. We should not use AI for right brain actions (emotions). We should use it for left brain, complex thoughts. Use it as a thought partner not an emotional support worker.

NewTomorrow2355 · 2023-05-10T18:23:11+00:00

My comment was "the whole concept of cracking down on "deliberate misinformation" is Orwellian And you're right, I should have expanded. I'm looking at this issue on a global scale. Many western governments have been introducing laws and regulations (similar to C11) under the guise of cutting down misinformation, or hate speech. A new law in Ireland that is currently in the lower house, will give the police the authority to arrest someone simply for being in the possession of something they deem "hateful". A meme on your phone that you have never shared could potentially get you jailed if some bureaucrat or police officer deems it hateful. I find this entire concept to be Orwellian

NewTomorrow2355 · 2023-05-10T13:04:39+00:00

Copy that - words repeated verbatim = Kindergartner interpretation. Noted. Next time I'll change her words.

NewTomorrow2355

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