MCP2026-08-184 min read

AI Visibility MCP for Cline

Cline lets you bring your own model — AgentGEO lets you bring your own GEO data layer. Add one MCP server to Cline's configuration and your agent can pull the raw answers ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Copilot and Gemini actually give: ask in plain language, get answer text plus source citations back as a tool result, right inside VS Code. One server, one deliberately narrow tool — fetch_raw_answers. It returns provider records unchanged; the ranking, diffing and fixing stay in Cline, in the same window as the markdown files that need the fixes.

In Cline, your agent can pull the raw answers the six engines actually give — ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Copilot and Gemini. Ask in plain language and the answer text and citations come back as a structured tool result, in the same VS Code window as the docs and landing-page files that need fixing. One MCP server, one narrow tool.

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Connect AgentGEO to Cline

Cline configures MCP servers in a JSON settings file. Open the MCP Servers panel and add an agentgeo entry to cline_mcp_settings.json:

{
  "mcpServers": {
    "agentgeo": {
      "command": "npx",
      "args": ["-y", "agentgeo-mcp", "--key", "ag_live_..."]
    }
  }
}

Create your ag_live_ key at /onboarding — free tier, no credit card. Want to try the wiring before spending anything? An ag_test_ key always returns clearly labelled demo records at zero credits. Reload, and the fetch_raw_answers tool appears in Cline's tool list.

Every call returns one record per surface — the provider's answer, verbatim, with every source it cited as structured data:

{
  "surfaceKey": "perplexity",
  "status": "delivered",
  "answerText": "...",
  "sources": [
    { "title": "...", "url": "https://...", "position": 1 }
  ],
  "fetchedAt": "2026-08-18T09:12:04Z",
  "latencyMs": 8412,
  "providerRecordId": "..."
}

What to ask once it's connected

  • "Fetch what chatgpt and perplexity answer for our main category query and tell me whether any cited domain is ours." The baseline visibility check, without opening a browser tab.
  • "Pull all six surfaces for the query our homepage targets, diff the sources[] lists, and write the result to geo-notes.md." The evidence lands in the repo it's about.
  • Use plan mode to have Cline compare Perplexity's citations against the pages under docs/ and propose which ones to tighten — then switch to act mode and let it edit the markdown.
  • "Check whether our pricing page appears in google_ai_overview sources for our product-plus-pricing query; if not, rewrite the page's opening to answer it directly."
  • "Diff gemini against copilot for the same query and list the competitor domains cited by both."
  • After the fixes ship, re-fetch the same query and diff answerText and sources[] against last week's record — check, fix, verify, all in one window.

Six engines, one contract. Diff the citation sets across chatgpt, perplexity, gemini, google_ai_overview, google_ai_mode and copilot — that diff is the core of GEO/AEO analysis, and Cline can run it right where you work, against the repo you fix it in.

What the MCP server gives you

  • One narrow toolfetch_raw_answers. No tool sprawl in your agent's context; one contract to learn.
  • Six engines behind one contract — the same arguments and record shape for every surface, so widening a check is one more string in surfaces.
  • Structured `sources[]` — title, URL and position for every citation, ready to diff, store or grep. No regexing links out of prose.
  • Raw records only — no rankings, sentiment or visibility scores baked in. The conclusions stay in your agent, next to your context.
  • Managed-scraper engine, maintained for you — the server talks to AgentGEO's API; you never babysit a headless browser or rotate proxies.
  • Usage billing with a spend cap — free tier, no credit card, one credit per delivered record, never per-seat.

What it doesn't do

AgentGEO is deliberately a GEO data layer, not a platform. The analysis belongs in the agent you already trust with your repo.

  • No dashboards, no scores, no recommendations engine. Cline — with whatever model you brought — does the ranking, diffing and fixing. That's the point.
  • Not instant. Live surfaces are slow: requests wait up to 180 seconds, and an AI Overview SERP round-trip alone runs 40–90s. Let the tool call finish.
  • Some fetches finish async. A slow chatbot scrape can exceed the sync budget and fail with a providerFields.snapshot_id; retry with that id and the same single surface to collect the finished answer without paying for a re-scrape (not valid for google_ai_overview).
Cline without AgentGEOCline + AgentGEO MCP
How answers get inCopy-pasted from six browser tabsOne fetch_raw_answers tool call
CitationsRetyped by hand, links lostStructured sources[] — title, URL, position
FreshnessWhatever was pasted lastFetched live, stamped fetchedAt
CoverageOne engine at a timeUp to six surfaces in one call
Cost of checkingYour attention, every timeOne credit per delivered record; failed = 0

AgentGEO is the answer-access layer. The analysis, the alerts and the fixes you build on it — in Cline, in scripts, in your product — are yours.

Prefer the REST API?

The same records are one HTTP call away: POST https://api.agentgeo.org/v1/fetches. See the Python and curl pages for the request shape — useful when a check graduates from an in-editor ask to a script committed next to your site.

Who builds on this

Developers who run Cline because it's open source and lets them bring their own model — and who edit their site or docs repo in the same VS Code window. If your GEO todo list is a stack of markdown files and your instinct is check it, then fix it, then commit it, wiring the data layer into the editor beats logging into someone else's dashboard.

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Frequently asked questions

How do I add the AgentGEO MCP server to Cline?
Open Cline's MCP Servers panel and add an agentgeo entry to the cline_mcp_settings.json configuration file with command: "npx" and args: ["-y", "agentgeo-mcp", "--key", "ag_live_..."]. The server is a zero-dependency npm package that speaks MCP over stdio on Node.js 18+; reload and the fetch_raw_answers tool appears in Cline's tool list.
What does fetch_raw_answers return in Cline?
One record per requested surface, unchanged from the provider: surfaceKey, status (delivered or failed), the full answerText, sources[] with title, URL and position, plus fetchedAt, latencyMs, providerRecordId and the raw providerFields. No rankings or scores — Cline does that part.
Does my code or analysis leave my machine?
The MCP server runs locally inside Cline over stdio. Your query text is sent to AgentGEO's managed scrapers to fetch the answers, and the raw records come back into your session. Your repo, your prompts and the analysis Cline runs on the records stay where they already live — on your machine, with the model you chose.
What does it cost?
One delivered record costs one credit; failed records cost zero. There's a free tier with no credit card required, then usage-based billing with a spend cap — never per-seat. ag_test_ keys return clearly labelled demo records at zero credits, handy for verifying the Cline wiring before you spend anything.
Which AI engines can Cline query through it?
Six: ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Copilot and Gemini — surface keys chatgpt, perplexity, gemini, google_ai_overview, google_ai_mode and copilot. Pick one to six per call; the record shape is identical across all of them.

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