AI Visibility MCP for Codex

In Codex, the raw answers the six engines actually give are one ask away: describe the check and fetch_raw_answers returns the answer text plus every cited source as a tool result. One MCP server, one deliberately narrow tool - and once it's added, a six-engine citation sweep is just another repeatable agent task, not a manual chore.

Connect AgentGEO to Codex

codex mcp add agentgeo -- npx -y agentgeo-mcp --key ag_live_...

That is the whole install. codex mcp add registers the server, npx pulls agentgeo-mcp from npm (stdio transport, zero npm dependencies, Node.js 18+), and the agentgeo tool is available to Codex's agent runs. A live ag_live_ key comes with a plan. Want to verify the wiring before fetching anything real? An ag_test_ key always returns clearly labelled demo records at zero credits.

When Codex calls fetch_raw_answers, each engine comes back as its own per-surface record - the raw answer, its sources in cited order, timing and provider metadata (the full record also carries the raw providerFields):

{
  "surfaceKey": "perplexity",
  "status": "delivered",
  "answerText": "...",
  "sources": [
    { "title": "...", "url": "https://...", "position": 1 }
  ],
  "fetchedAt": "2026-07-14T09:12:04Z",
  "latencyMs": 8412,
  "providerRecordId": "..."
}

What to ask once it's connected

No arguments to memorize - describe the task and Codex shapes the call. Say "in Japan" and it passes country through (it defaults to US). Worth knowing: language only reaches Google AI Overviews, where it drives the search locale - the chatbot surfaces take a country and no language field. Asks that make good repeatable agent tasks:

• "Fetch what ChatGPT and Gemini answer for 'best CI/CD for monorepos' and list which domains each cites."
• "Run our top three category queries across all six surfaces and report which ones cite ourdomain.com, with positions."
• "Pull Perplexity's answer for our main query, compare sources[] against geo-baseline.json, and print what entered or dropped out."
• "Diff google_ai_overview against google_ai_mode for 'best feature flag tools' and summarize the difference in cited sources."
• "Fetch Copilot's answer for 'alternatives to our product' - are we in the answer text, or only in the sources?"
• "Sweep all six engines for our five money queries and write a citation table to reports/geo-$(date).md." Wire that into your task runner and the check repeats itself.

What the MCP server gives you

Deliberately thin - an answer-access layer for your agent, not an analytics product bolted onto your CLI.

• One narrow tool - fetch_raw_answers posts to AgentGEO's API and returns the run envelope verbatim. Nothing else to learn, nothing else to break.
• Six engines behind one contract - ChatGPT, Perplexity, Gemini, Google AI Overviews, Google AI Mode and Copilot with an identical record shape, so adding an engine to a task is one more word in the ask.
• Structured sources[] - every citation with its title, URL and position, ready for the agent to diff, count or grep against your domain. No links regexed out of prose.
• Raw records only - no rankings, sentiment or visibility scores. The conclusions come from Codex, with your project in context.
• Managed-scraper engine, maintained for you - under the hood the server talks to POST https://api.agentgeo.org/v1/fetches; keeping six collection paths healthy is AgentGEO's job, not a headless browser in your task runner.
• Usage-based billing - one credit per run, failed runs cost zero, priced by usage rather than per seat.

What it doesn't do

The narrowness is the point. Codex holds your project and your intent; conclusions computed outside that context would be worth less than raw records analyzed inside it - that is the whole GEO data layer argument. So, honestly:

• No dashboards. Records arrive as tool results; the report is whatever Codex writes from them, in your repo or your task output.
• No visibility scores or sentiment. Raw answerText and sources[] only - the judgment layer is deliberately left to your agent.
• No recommendations engine. Codex reasons over your actual pages, which beats generic advice every time.
• Not instant. Requests wait up to 180 seconds - live surfaces are slow. An AI Overview SERP round-trip runs 40-90s, and some chatbot scrapes exceed the sync budget entirely: those records come back failed with a providerFields.snapshot_id, and a follow-up call with that id and the same single surface redeems the finished answer without paying for a re-scrape (not valid for google_ai_overview). Budget for it when the task is unattended.

Prefer the REST API?

The MCP server is a thin wrapper over the same endpoint your scripts can hit directly: POST https://api.agentgeo.org/v1/fetches. See the Python and curl pages for the request shape - handy when an agent task graduates into a plain cron job with the identical contract.

Who builds on this

Developers who drive Codex for agentic tasks and want answer-engine checks to be one more thing the agent just does. Add the server once, and asking Codex to sweep the six engines for your category - and diff the citations against last week - becomes a repeatable task rather than a manual afternoon. See where you stand first with the free audit, or see how the approaches compare.