Claude for Chrome

Having played a LOT with browser use, playwright, and puppeteer (all via MCP integrations and pythonic test cases), it's incredibly clear how quickly Claude (in particular) loses the thread as it starts to interact with the browser. There's a TON of visual and contextual information that just vanishes as you begin to do anything particularly complex. In my experience, repeatedly forcing new context windows between screenshots has dramatically improved the ability for claude to perform complex intearctions in the browser, but it's all been pretty weak.

When Claude can operate in the browser and effectively understand 5 radio buttons in a row, I think we'll have made real progress. So far, I've not seen that eval.

I have built a custom "deep research" internally that uses puppeteer to find business information, tech stack and other information about a company for our sales team.

My experience was that giving the LLM a very limited set of tools and no screenshots worked pretty damn well. Tbf for my use case I don't need more interactivity than navigate_to_url and click_link. Each tool returning a text version of the page and the clickable options as an array.

It is very capable of answering our basic questions. Although it is powered by gpt-5 not claude now.

Seems navigate_to_url and click_link would be solved with just a script running puppeteer vs having an llm craft a puppeteer script to hopefully do this simple action reliably? What is the great advantage with the llm tooling in this case?

Oh the tools are hand coded (or rather built with Claude Code) but the agent can call them to control the browser.

Imagine a prompt like this:

You are a research agent your goal is to figure out this companies tech stack: - Company Name

Your available tools are: - navigate_to_url: use this to load a page e.g. use google or bing to search for the company site It will return the page content as well as a list of available links - click_link: Use this to click on a specific link on the currently open page. It will also return the current page content and any available links

A good strategy is usually to go on the companies careers page and search for technical roles.

This is a short form of what is actually written there but we use this to score leads as we are built on postgres and AWS and if a company is using those, these are very interesting relevancy signals for us.

I still don't understand what the llm does. One could do this with a few lines of curl and a list of tools to query against.

The LLM understands arbitrary web pages and finds the correct links to click. Not for one specific page but for ANY company name that you give it.

It will always come back with a list of technologies used if available on the companies page. Regardless of how that page is structured. That level of generic understanding is simply not solveable with just some regex and curls.