Run SEO from your AI assistant: the MCP workflow

What is MCP, and why does it matter for SEO?

MCP, the Model Context Protocol, is the standard that lets an AI assistant call tools. Without it, an assistant knows what it was trained on and what you paste in. With it, the assistant can ask a server a question and get the answer as data: the queries your site ranked for last month, the search volume for a term, the position of a keyword on mobile in one city, the pages a crawl found with no title.

For SEO the difference is the whole job. Most of the work is not writing; it is moving between five tabs, exporting from one and pasting into another, then thinking. An assistant with the data in the conversation does the moving and leaves you the thinking.

Once the servers are connected, a morning can start with the assistant going through every connected account and pulling the data, so you see at a glance what needs doing for each client. A first diagnostic for a new site can run the same way: crawl, keyword data and Search Console, pulled through MCP in one pass. And it changes how you use the other tools too: it becomes easier to ask the assistant to create or check a ticket than to log into the ticket tracker yourself. Read the data through one server, write through another, and the assistant is the thing in between.

The setup, once

The SEO Scaler MCP connects to Claude Code, Codex, Cursor and other MCP clients. Create an API key in the app, store it as SEO_SCALER_API_KEY, and add the hosted endpoint as a server named seo-scaler. The assistant then gains the tools: Search Console reads, keyword metrics, SERP results, domain and backlink data, rank tracking, site audit, local rank grids. Any call that costs credits shows its cost first, and nothing spends until you approve.

The workflows themselves are skills, built into the SEO Scaler agent in the app; there is nothing to install. There are skills for keyword research, competitor analysis, site audit, local SEO and link prospecting, plus an SEO coach that picks the workflow if you are not sure which you need. An external assistant connected through MCP has the same data tools; ask it for one focused job in plain words, and the skill pages describe what a good result looks like.

Start with project setup. It saves your goals, positioning, competitors and key pages to the project, and every later prompt reads them.

The first five prompts

Five prompts, in order, that return more than they cost. Each one is a full strategy elsewhere in this library; these are the short forms.

1. What am I already ranking for that I am not getting? Search Console by query, last three months, positions 4 to 20, sorted by impressions. This is the list a rank tracker is built from.

2. Which of my pages compete with each other? Search Console by query and page. Flag queries with multiple URLs for review. Check whether those pages serve the same intent and whether the overlap hurts performance before changing links or merging them. Cluster keywords into topical hubs covers it.

3. What do the sites that outrank me have that I do not? Domain overview and ranked keywords for the top three competitors, minus yours. Keyword gap analysis.

4. What is broken? A site audit, then the issues sorted by severity. The technical SEO audit checklist.

5. Where should my links come from? Your own top pages by referring domains, and the reason each one earned them. The backlink checker lists them.

None of the five needs a keyword typed by a human. All five need a human to read the answer.

The row that shows why

Run the first prompt and read the whole table, not only the top. The top rows are what you would expect: the brand name, a few product terms, a query on page two with a healthy number of impressions.

Further down, Search Console can record queries that no person typed: a string of a thousand or more characters that reads like an AI system prompt, beginning something like "you are a keyword analysis bot", sitting at position 10 with a few dozen impressions. That is another company's AI agent pasting its own instructions into Google, and Google recording the site as ranking for it.

An assistant that summarises the table will count that row. A person who reads it will laugh and exclude it. The assistant is fast and eager, and it still lacks judgement. The data arrives faster than you could fetch it. The judgement about what it means did not get faster, and it is still yours. What to automate and what to keep is about where that line goes.

Run this with the SEO Scaler MCP

Connect Search Console to the project and the SEO Scaler MCP to your assistant. This prompt is the first of the five above, with the filter step that rows like that make necessary.

Using the SEO Scaler MCP on project [name]:

1. Call get_project_context and tell me what the project already
   knows: goal, positioning, competitors, key pages. If it is empty,
   stop and ask me the four questions the seo-project-setup skill
   asks.

2. Pull Search Console performance for the last 3 months by query,
   all rows. Exclude queries containing the brand name and any query
   longer than 80 characters or containing "you are a" or a numeric
   ID followed by a colon. Tell me how many rows you excluded and
   show me three of them.

3. Keep queries at average position 4 to 20 with at least 100
   impressions. Sort by impressions. Show me the top 20 with clicks,
   impressions and position.

4. Pull the same period by query and page, and for the 20, tell me
   which URL ranks and whether any query shows two of my URLs.

5. Do not recommend content yet. Tell me which three of the 20 you
   would want to look at first and why, in one line each.

Step 2's exclusion count is the tell. Without it, bot queries can sit in the striking-distance list forever, at position 10, waiting for clicks that no human will ever send.

The assistant fetches; you decide

MCP turns the assistant from a writer into an analyst with access. That is most of the value of the tools you were paying for, in a window you already have open. What it does not turn the assistant into is the person who knows that a two-thousand-character query is a bot, that a position-2 term with no clicks has an answer box above it, or that the client's boss will only read the first sentence. Those stay with you, and the rest of this library is about keeping them there.

MCP for SEO FAQ

What is MCP in SEO?

The Model Context Protocol, a standard that lets an AI assistant call external tools and receive data. An SEO MCP server gives the assistant Search Console, keyword, SERP, backlink, rank tracking and audit data inside the conversation, so it can fetch and analyse instead of only writing from what you paste.

Which AI assistants work with the SEO Scaler MCP?

Claude Code, Codex, Cursor and other MCP clients, through one hosted endpoint and an API key. The MCP docs carry the setup for each. The skills run in the SEO Scaler agent in the app; an external assistant gets the same data tools.

Does using the MCP cost credits?

Search Console reads, URL inspection and audit reads use no credits. Calls that fetch from a data provider, such as keyword metrics, SERP results, domain and backlink data, and rank checks, use credits. The cost is shown before anything runs, and nothing spends until you approve.

Can an AI agent do SEO on its own?

It can fetch, filter, sort and draft on its own. It cannot tell a bot query from a human one, judge whether a ranking is worth having, or know what the business will act on. The workflows here keep a person at the steps where that judgement happens.

What is the first thing to run after connecting the MCP?

Project setup, then a Search Console pull of queries at positions 4 to 20 with real impressions, with a filter that removes bot queries and junk. It costs nothing and it produces the list every other workflow starts from.