Floyi MCP Server: Run SEO From a Chat

Real SEO work is a series of tool round-trips.

Check the map in one tab. Pull the SERP in another. Find the gaps in a third. Write the brief somewhere else. Copy it into a doc. Hand it to a writer. Come back and check the score.

Every switch drops a little context. By the time the draft exists, half of what you knew when you started is gone.

Floyi was built to end that. Brand, map, briefs, drafts, publishing, and authority tracking live in one platform, one closed loop, so the strategy never leaks between tools.

But someone still has to drive it. You open the tabs, run each step, and read the results, screen by screen.

What if you could just ask for the outcome instead?

What the Floyi MCP Server Is

MCP, the Model Context Protocol, is an open standard that lets AI assistants securely use external tools. Anthropic introduced it, and it is quickly becoming the way assistants talk to real software.

Floyi now speaks it.

You connect an AI assistant to your Floyi workspace, Claude Code, Claude Desktop through a local bridge, or any client that supports the protocol, and the assistant gets 99 tools covering the full pipeline. Brand setup, personas, site architecture, topical research, clustering, map building, briefs, drafts, quality review, publishing, and authority monitoring.

You stop clicking through the app. You describe what you want, and the assistant calls the right tools to do it.

It is in beta on the Scale plan while we harden it.

What It Actually Does

The power is not in single tool calls. It is in the flow of those tools. The assistant chains tools together to run the workflows an SEO team actually runs.

A few real prompts.

Find the work. “Find the biggest content gaps for acme.com.” The assistant ranks them across your map: missing content, thin coverage, pages ranking past position 20. Missing is your net-new queue. The rest is your refresh queue.

Do the work. “Generate a content brief for the top gap using the SERP results 1, 2, and 4.” Floyi generates both natively and reads them back to you.

Run the whole pipeline. From nothing to a live map in one session:

create the brand -> generate personas -> build the site architecture -> run topical research -> cluster the keywords -> build the map -> advance to Topical Authority

Report on it. “Generate a weekly report for acme.com.” Pipeline status, top gaps, and progress in one summary. I have built maps for 300+ brands, and the reporting was always the tax. Not the strategy. The round-trips. For an agency running ten clients, that Monday-morning digest becomes something you ask for instead of assemble.

One thing to be clear about, because it is the part people get wrong.

When you ask for a brief or a draft, the assistant does not write it. Floyi does.

The tool call triggers Floyi’s own generation pipeline, the exact same process as clicking Generate in the app. The draft lands in your workspace with the correct sections, internal links, and map position.

That is the difference that matters. Raw chat prose carries none of the enforcement that makes content ready to ship.

A Floyi draft runs through your Content Guide rules, your analyzed brand voice, and your buyer personas. It targets the entities and coverage terms from your brief, pulls context from your Knowledge Base, and can run specialist agents for research and fact-checking. It comes back node-bound and in the editor, tied to the exact topic in your map.

It also inherits the brief’s information gain, so it adds something the top results do not already say.

Ask a chatbot to write the article and you get fluent text that honors none of that. You are not asking a chatbot to imitate Floyi. You are triggering Floyi to do the work, then reading the result.

Why This Matters for GEO and AEO

Generative engine optimization and answer engine optimization have a measurement problem. The surfaces move fast, the queries are conversational, and checking them by hand does not scale.

An assistant that can drive Floyi closes that loop.

You can run an AI Search Gaps analysis to see what the AI engines cover that your map does not. You can refresh authority data and ask where you are cited or only mentioned across AI Overviews, AI Mode, ChatGPT, and Gemini. You can generate a brief and its first draft for the gap you just found, without leaving the conversation.

The strategy, the production, and the measurement happen in the same place. That is the whole argument for a closed loop, now available through a chat.

Credits and Guardrails

Two questions always come next.

Does it cost more? No. There is no connection fee and no per-call charge. Reading your map, finding gaps, and checking status are free, no matter how often the assistant calls. Credits are spent only when you trigger a paid action like brief or draft generation, at the same price as the app. Paid tools check your balance first and the assistant confirms the cost before starting.

Can it break something? The guardrails are strict. High-impact actions require confirmation. Map edits snapshot first and are revertible. WordPress publishing defaults to draft status, so nothing goes live without review. GitHub commits make the assistant confirm the repo and branch. Full rich-text editing, visual review, and version restoration stay in the app on purpose.

For the complete list of what you can ask, the example prompts for every workflow page is a real script you can copy. For the underlying capabilities, browse the full tool catalog.

Where This Goes

The interface to software has been a screen full of buttons for thirty years. That is starting to change.

For SEO, the shift is bigger than convenience. When the strategy layer, the production layer, and the measurement layer are all callable from one conversation, the busywork between them disappears. You spend your attention on the decisions, not the round-trips.

It is in beta and available to everyone on the Scale plan. Connect your assistant and go.

What would you build if your entire SEO pipeline answered to a sentence?