The short version
- An SEO tool knows what is wrong. An AI coding assistant can change it. Neither does the other job well.
- An AI model’s general knowledge is not a live search dataset. Use connected tools or supplied exports for volumes, rankings, competitors and backlinks.
- Left to general knowledge, an AI assistant will confidently produce outdated SEO advice. Grounding it matters.
- The realistic stack is free diagnostic tools plus an AI coding assistant, adding a paid suite when questions become comparative.
Developers who have started using AI coding assistants often notice something: when they ask one to improve a page’s SEO, it does the work. It writes the title tag, adds the structured data, restructures the headings, adds the internal links. Whereas the SEO platform they pay for every month produces a list of things that need doing.
That observation is correct and it is also incomplete. The two are good at genuinely different things, and the failure modes of relying on either alone are worth understanding before you cancel anything.
The short answer
Fit for your workflow
Diagnose with SEO tools, implement with the coding assistant.
Use free or paid SEO sources to diagnose problems and research demand. An assistant can analyse connected tools or supplied exports, but its general knowledge cannot establish current volumes or rankings. Use the coding assistant to implement reviewed changes where it has code access; check what your SEO platform can implement before assuming it only reports.
- Developer who owns the site
- Free diagnostics plus a coding assistant. Add a paid suite later.
- Marketer without code access
- SEO tool only. The coding assistant is not usable for you.
- Agency
- Paid suite for diagnosis and reporting; coding assistant where you have repo access.
What each is good and bad at
The last row is the structural advantage and it is easy to underrate. An SEO crawler sees the rendered HTML of a page. An AI coding assistant sees the component that generated it, which means a fix applied once to a template corrects every page that uses it, rather than being logged as 400 separate issues.
Where the AI assistant fails badly
- Invented data. Asked for search volume it does not have, a model may produce a plausible number. Treat any figure it offers about search behaviour as fabricated unless it came from a tool.
- Outdated practice. General training includes a great deal of SEO advice that stopped being true years ago: keyword density targets, meta keywords, exact-match repetition.
- Missing ranking history. Without a connected source or supplied report, it cannot establish whether your positions changed last week.
- Over-optimisation. Asked to optimise a page, an ungrounded model will often stuff keywords in a way that makes the page worse. See keyword density vs semantic coverage.
- Confident wrongness. It may present guesses as facts. Check the cited evidence rather than relying on its confidence.
Those failure modes are the argument for grounding the assistant in something real rather than relying on general knowledge, whether that is a maintained set of SEO instructions, your own documented standards, or simply pasting in the actual audit output and telling it to work from that and nothing else.
Grounding the assistant so it stops guessing
The failure modes above all share one cause: the model is working from general knowledge rather than from anything specific to your situation. That is fixable without buying anything. Paste your actual crawl output, your Search Console query export, and a short statement of your own standards into the context, and instruct it to work only from those.
A useful third input is a primary source. Giving an assistant Google’s SEO starter guide or the relevant structured data documentation can reduce reliance on outdated recollection. Check that the assistant actually used the source and that the source applies to your case; providing documentation does not guarantee a correct answer.
This is the category we build in, so read accordingly
Our product is an MCP server that gives an AI coding assistant a maintained set of SEO instructions. The obvious bias is that we benefit from you believing the grounding matters. The checkable version: the failure modes listed above are reproducible in any assistant in a few minutes, at no cost, and you can address them by pasting your own standards into the context rather than buying anything.