{"id":208764,"date":"2026-03-03T14:56:58","date_gmt":"2026-03-03T14:56:58","guid":{"rendered":"https:\/\/www.similarweb.com\/blog\/?p=208764"},"modified":"2026-03-03T15:18:12","modified_gmt":"2026-03-03T15:18:12","slug":"automate-keyword-research-with-similarweb-mcp","status":"publish","type":"post","link":"https:\/\/www.similarweb.com\/blog\/marketing\/geo\/automate-keyword-research-with-similarweb-mcp\/","title":{"rendered":"How I automated GEO keyword research with Claude and the Similarweb MCP"},"content":{"rendered":"

The <\/span>GEO keyword research article<\/span><\/a> I published a few days ago described a seven-part process for restructuring keyword research around how LLMs actually search (the FAN methodology). The response I got most was some version of: “This makes sense. How long does it take?”<\/span><\/p>\n

Honest answer: The first time I ran it manually, it took 4 hours. Per topic cluster.<\/span><\/p>\n

That is not a scalable content operation. So I rebuilt the workflow as an automated AI agent keyword research pipeline:\u00a0<\/span><\/p>\n

Claude connected to the <\/span>Similarweb MCP server<\/span><\/a>, with a custom skill that executes the full FAN methodology: anchor query intake, fan-out mapping, Similarweb keyword validation, content gap identification, and brief generation in a single conversation.\u00a0<\/span><\/p>\n

\"GEO<\/p>\n

The whole run now takes under 20 minutes, and the output is richer than anything I was producing manually.<\/span><\/p>\n

This article is the operational companion to the FAN methodology guide. I am going to walk through exactly how I set it up, what the stack looks like, and what the output produces that manual keyword research cannot.\u00a0<\/span><\/p>\n

If you have not read the FAN methodology for GEO keyword research piece first, I recommend doing that before continuing, since the framework vocabulary (fan-out mapping, node architecture, anchor queries) is assumed throughout.<\/span><\/p>\n

Why I needed to automate this in the first place<\/span><\/h2>\n

Running the FAN methodology manually means doing seven distinct things for every topic cluster you want to cover:<\/span><\/p>\n

    \n
  1. Define a 15-to-25-word anchor query from real audience language<\/span><\/li>\n
  2. Map the seven sub-query types (Definition, Comparison, How-to, Use case, Objection, Entity expansion, Metric)<\/span><\/li>\n
  3. Write the specific sub-query for each type<\/span><\/li>\n
  4. Validate each sub-query against Similarweb keyword data<\/span><\/li>\n
  5. Cross-reference the full map against existing published content<\/span><\/li>\n
  6. Flag coverage gaps and assign priority (standalone article vs. section-level coverage)<\/span><\/li>\n
  7. Output a GEO brief with all data pre-populated<\/span><\/li>\n<\/ol>\n

    Steps 1 and 2 require judgment. Steps 3 through 7 require data access and pattern matching, which is exactly what a well-configured AI agent does faster and more consistently than any human working through a spreadsheet.<\/span><\/p>\n

    The business case is straightforward. I manage content strategy for Similarweb’s GEO topic cluster. At any given time, I am tracking 15 to 20 active topic clusters. Manually running the FAN methodology across all of them would require roughly 60 to 80 hours per cycle.\u00a0<\/span><\/p>\n

    The automated workflow runs all 20 in an afternoon. That is not a minor efficiency gain. It is the difference between the methodology being practical and it being theoretical.<\/span><\/p>\n

    What the Similarweb MCP is and why it matters for this specifically<\/span><\/h2>\n

    The Similarweb MCP is a connector that gives Claude direct, real-time access to <\/span>Similarweb’s keyword intelligence<\/span><\/a> data mid-conversation: search volume, keyword difficulty, zero-click rate, intent distribution, click share, related keyword clusters, and more. All without any manual exports or context switching.<\/span><\/p>\n

    \"Similarweb<\/p>\n

    The Model Context Protocol<\/a> (MCP) is an open standard developed by Anthropic that enables AI assistants to connect to external data sources and tools mid-conversation. You ask Claude a question, Claude calls the data source, gets the answer, and continues its reasoning, all in one thread.<\/span><\/p>\n

    This matters specifically for GEO keyword research given the nature of fan-out subqueries. Most <\/span>fan-out sub-queries<\/span><\/a> are long, conversational, and low volume (exactly the kind of terms that traditional keyword tools undercount or miss entirely).\u00a0<\/span><\/p>\n

    They are also fast-moving: a sub-query with no search volume six months ago may now have 1,200 monthly searches because the topic broke into mainstream discussion. <\/span>Pulling live data from Similarweb at the moment of research, rather than from a cached export<\/b>, means the brief reflects current reality.<\/span><\/p>\n

    Setting up the stack: three steps, no coding required<\/span><\/h2>\n

    Setting up the Claude and Similarweb MCP stack requires three steps and no code: connect the Similarweb connector in Claude\u2019s settings (about two minutes), upload the GEO keyword research skill file, and provide one anchor query. Everything after that runs automatically.<\/span><\/p>\n

    The setup is simpler than it looks. You do not need to write any code or touch a config file.<\/span><\/p>\n

    Step 1: Connect the Similarweb MCP to Claude<\/span><\/h3>\n

    In Claude’s settings, navigate to \u2018Connectors\u2019 and find the Similarweb connector, or go through \u2018Customize\u2019 and choose \u2018Connect your tools\u2019, and then find the connector. It authenticates via your Similarweb account credentials.\u00a0<\/span><\/p>\n

    Once connected, Claude can call Similarweb keyword data (and much more) directly from any conversation. The connection takes about two minutes to configure.<\/span><\/p>\n

    \"Similarweb<\/p>\n

    Step 2: Load the GEO keyword research skill<\/span><\/h3>\n

    A Claude skill is a pre-built instruction set saved as a file that Claude loads at the start of a task (see <\/span>Anthropic Agent Skills documentation<\/span><\/a>).\u00a0<\/span><\/p>\n

    My GEO keyword research skill includes the full FAN methodology:\u00a0<\/span><\/p>\n