Connect Lumos to Gemini via MCP: Live AI Visibility Stream

What is the Model Context Protocol (MCP)?

The Model Context Protocol is an open standard, originally released by Anthropic in late 2024, for connecting AI assistants to external data sources. Instead of copy-pasting reports into a chat window, MCP lets an AI agent call live tools — read data, query an API, fetch documents — directly inside the conversation. The protocol is now adopted by Claude natively, supported in ChatGPT via OpenAI's custom-server configuration, and supported in Gemini via Google's official MCP adapter.

For a product like Lumos, MCP is the cleanest way to make AI visibility data usable: instead of asking your marketing team to log in and export a CSV, you ask Gemini directly and it pulls fresh data from Lumos in the same conversation.

Why connect Lumos to Gemini?

When the Lumos MCP server is connected, Gemini can read your live workspace data inside any conversation. That unlocks several workflows that were previously friction-heavy:

• Morning briefings. Ask Gemini "summarize my Lumos visibility this week" and it pulls daily scores, top movers, and competitive deltas without leaving the chat.
• Ad-hoc competitive checks. "How am I doing against HubSpot in ChatGPT for CRM prompts?" — Gemini queries Lumos's competitor data and answers in plain English.
• Content workflows. When you're drafting a page with Gemini, you can ask it to incorporate Lumos's recommended actions for that page — improving citability while you write.
• Briefings for non-Lumos users. Sales engineers, leadership, and content writers can query the data via Gemini without ever touching the Lumos dashboard.

How the connection works

The setup takes about three minutes.

1. Sign up for Lumos Pro. Start with the signup flow. The free tier doesn't include the MCP server — Pro and above do, and Pro starts with a 14-day no-credit-card trial.
2. Generate an MCP API key. In your Lumos dashboard, open Settings → Integrations → MCP. Click "Generate key" and copy the value. The key is scoped to your current workspace and is read-only.
3. Add the server to Gemini. Google ships an official MCP adapter for Gemini. In Gemini Advanced or your Google AI Studio workspace, navigate to Extensions or Connectors, choose "Add custom MCP server", paste the Lumos MCP URL (provided in your dashboard alongside the key) and your API key.
4. Test the connection. Start a new Gemini conversation and ask: "summarize my Lumos visibility this week". Gemini will call the MCP server, fetch your latest scan, and answer with live data.

What data is exposed via MCP?

The Lumos MCP server exposes the read-only surface of your dashboard, mapped to MCP tools that Gemini can call:

• Daily visibility score per engine (ChatGPT, Gemini, Claude, Perplexity, Google AI).
• Mentions over time, including sentiment and the themes AI engines associate with your brand.
• Competitor head-to-head — who outranks you on which prompts, and by how much.
• The recommended actions backlog — Lumos's prioritized weekly list of pages to fix.
• Page-level citability scores for every page Lumos has graded.

What is not exposed: account settings, billing, team management, or any write action. The MCP server cannot change your Lumos configuration — it can only read.

Security notes

MCP API keys are read-only by default. Each key is scoped to a single Lumos workspace, so if you have separate workspaces for staging and production, you can issue separate keys for each. Keys are revocable from the dashboard with one click — revoking immediately disconnects any Gemini (or other MCP client) using that key. We also log every MCP call to the audit trail in your workspace, so you can see exactly which queries were made and when.

Other MCP-friendly LLMs

The Lumos MCP server works with any MCP-compatible client. Today that includes Claude (see Connect Lumos to Claude), ChatGPT (see Connect Lumos to ChatGPT), Gemini (this page), and a growing number of agentic frameworks. The same API key works across all clients — you don't need a separate key per LLM.