Keyword Research Inside Your AI Chat: An MCP Walkthrough for Small Teams

Li Chris — Wed, 14 Oct 2026 03:01:21 +0000

Last month we looked at our Search Console queries and found something odd: people were searching for "keyword research mcp" and "seo tools mcp" — and finding almost nothing worth clicking. Real demand, thin results.

So here's the walkthrough we wish existed when we started. The same steps work with any MCP server that exposes keyword data.

What MCP changes about keyword research

Without MCP: you think of a topic in your chat, switch to a keyword tool tab, export a CSV, paste it back, and lose the thread. With MCP: your AI client calls the keyword tool directly and you stay in one conversation. The research stops being a detour and becomes part of the thinking.

A concrete 15-minute workflow

Seed list from the customer's own words. Paste the last ten client emails into the chat and ask for the recurring phrases buyers actually use — not the industry jargon. This is your raw seed list, 15-25 phrases.
Expand and score in one pass. Ask your client to pull search volume and difficulty for the seed list plus two levels of expansion. Read the numbers in the chat, not in a spreadsheet. Kill anything with difficulty above your site's realistic range — for a small team site that's usually anything above 40.
Cluster by intent, not by topic. "b2b marketing automation" and "best b2b marketing automation platform" are different intents — comparison versus category. Ask the chat to group keywords by what the searcher is trying to decide. One page per decision, not per keyword.
Check the AI layer. Ask two or three AI assistants who they'd recommend in your category. The queries they cite are effectively tomorrow's keyword list — that's where buying research is moving.

What the numbers should warn you about

We ran this on our own site this week. Three months of data: 3,770 impressions, 10 clicks, average position 59.6. Last 28 days: 25 impressions. The lesson wasn't "write more content" — it was that one broad head term we were drifting toward was already lost (311 impressions, zero clicks). The terms we could actually win were the specific, tool-adjacent ones with a fraction of the volume. Small teams win long-tail decisions, not categories.

Common failure modes

Scoring keywords before reading the customer's language. The data tool doesn't know your buyer says "research hours" when the industry says "market intelligence."
Clustering by similarity instead of intent. Similar words, different decisions.
Ignoring the AI-citation layer entirely. It's the only channel growing while classic organic flattens.

If you maintain an MCP server yourself, one practical note: health-check uptime matters more than you think. Directory crawlers test endpoints continuously, and a server that fails auth checks gets marked degraded in listings within days.

Disclosure: we build a marketing-data MCP server (SupaMarketers Data) and run supamarketers.com. The expand-and-score step in the workflow above is what our Keyword Research app does — you can see it end to end there. This article is our honest workflow — the same steps work with any equivalent tool.

We Found ChatGPT Referrals in Our Analytics — Here's What Small Teams Should Do

Li Chris — Tue, 13 Oct 2026 09:46:18 +0000

This week our Google Analytics started showing something new: chatgpt.com / ai-assistant as a source/medium row. Two visitors. Tiny, right? But it's the first time "an AI answered a question and someone clicked through to us" showed up as a measurable channel.

What it looks like in GA4

Reports → User acquisition, then switch the primary dimension to First user source/medium — that's the table where chatgpt.com / ai-assistant showed up for us. (Traffic acquisition has the session-scoped equivalent under Session source/medium.) Since May 2026, GA4 also ships a native AI Assistant default channel group, so ChatGPT, Copilot and Gemini referrals get their own line instead of hiding in "Referral". Perplexity is not in that native definition yet — perplexity.ai / referral still folds into Referral, which makes it the quiet one to watch.

Three things worth doing this week

1. Segment AI sources explicitly. The native channel catches some of them, but one segment gives you the unified view:

chatgpt.com|chat.openai.com|perplexity.ai|copilot.microsoft.com|gemini.google.com

Watch it weekly like you'd watch a new paid channel.

2. Check what AI says about you. Ask the 3-4 questions your buyers actually ask ("best [your category] for small teams") in ChatGPT and Perplexity. Note whether you appear, and who does.

3. Feed the answers, not just the crawler. Getting mentioned in AI answers correlates with being cited in the sources they retrieve: comparison pages, listicles, Reddit threads, structured FAQs on your own site.

Why this matters more for small teams

Enterprise brands will buy visibility tooling. Small teams can win by being early: your competitors' analytics probably don't even have this segment yet. The cost of checking is ten minutes; the cost of being invisible in the shortlist phase is the whole deal.

Disclosure: I'm building SupaMarketers, a toolkit that helps small marketing teams with research, content, and tracking things like AI visibility. This post is the observation, not a pitch — the GA4 steps above work with or without any tool.

DEV Community: Li Chris

Keyword Research Inside Your AI Chat: An MCP Walkthrough for Small Teams

We Found ChatGPT Referrals in Our Analytics — Here's What Small Teams Should Do

What it looks like in GA4

Three things worth doing this week

Why this matters more for small teams