Foglift gives an AI-native marketer, founder, or operator the data layer their agent needs: high-intent prompt checks, citation and source history, competitor context, recommendations, API access, CLI workflows, and MCP tools.
An agent can draft pages, audits, social replies, and outreach. It still needs a reliable memory of where the brand appears, which sources AI engines cite, and what should happen next.
A founder can ask an agent what to publish, which citation gap matters, or which newsletter deserves a pitch. Foglift supplies the prompt, citation, competitor, and recommendation data behind that decision.
One prompt may cite your homepage while another recommends a competitor. Agents need repeatable checks across ChatGPT, Perplexity, Claude, Gemini, and Google AI Overview.
Generic marketing advice is not enough. The agent needs cited URLs, source history, competitor mentions, and page-level fixes so it can draft a useful edit or pitch.
The complete agent workflow: detect the traffic signal, check visibility, inspect sources, choose the move, ship the artifact.
The agent watches the traffic signal first. Referrer analytics show visits arriving from ChatGPT, Claude, Perplexity, Gemini, and Copilot - the daily trend, the engine mix, and the landing pages they cite. A flat month is the trigger; the rest of the loop is the diagnosis.
// Agent checks the AI referral trend
const referrals = await foglift.getReferrerAnalytics({ days: 90 });
// MCP: get_referrer_analytics
// Returns: total visits, per-engine breakdown,
// top landing paths, daily timelineSame calls, three surfaces: MCP tool names match the REST endpoints and CLI commands.
Agent runs a high-intent prompt across engines and sees whether the brand appears, who wins instead, and which pages were cited.
// Agent runs an AI Visibility Check
const visibility = await foglift.runAiVisibility({
domain: "example.com",
prompts: ["best AI search monitoring tool for brands"],
models: ["chatgpt", "perplexity", "claude", "gemini"]
});
// MCP: run_ai_visibilityThe agent reads citation history, source URLs, competitor mentions, sentiment, and recommendation context before drafting anything.
// Agent pulls the evidence layer
const results = await foglift.getAiResults({
days: 30,
model: "chatgpt",
prompt: "best AI search monitoring tool for brands"
});
// MCP: get_ai_resultsFoglift recommendations turn the evidence into a ranked action list: refresh a comparison page, add a citation table, pitch a source-rich report, or adjust positioning.
// Agent asks what to do next
const diagnostics = await foglift.getRecommendations();
// MCP: get_recommendations
// Returns: prioritized pages, prompts, competitors, and rationaleThe agent drafts the page edit, outreach note, social reply, or internal brief. The human reviews the artifact, then the next visibility check measures prompt movement; the referrer timeline confirms whether it became visits.
// Agent drafts from live evidence
const draft = await agent.writePitch({
sourceAsset: "/research/ai-search-citation-benchmark-2026-q2",
target: "editorial newsletter",
evidence: results
});Winning a prompt is not one job - it is five. Foglift's engine source profiles show which source layer each model leans on for your prompts, and which domains are distinctive to a single engine.
YouTube, Reddit, and LinkedIn dominate its citations. If those channels are empty, so is your Perplexity presence.
Directories, automation galleries, and tool ecosystems appear in Claude's citations and almost nowhere else.
A small set of anchor domains supplies most of its answers for a given prompt. The agent's job is to find them and get you into them.
// Agent pulls per-engine source profiles
const diagnostics = await foglift.getRecommendations();
// MCP: get_recommendations
// diagnostics.engine_source_profiles: top domains,
// distinctive domains, and source categories per engineThe agent routes the work accordingly: a video for one engine, a directory listing for another, an anchor-page pitch for a third.
Foglift's demand-gen agent uses the same loop on foglift.io. It checks high-intent AI answers, reviews cited pages and competitor mentions, filters for places where Foglift research strengthens the article, drafts founder-review outreach, and logs the result.
This is not hypothetical: the same loop recently diagnosed a referral plateau on foglift.io, traced it to a third-party citation gap, and produced the task list now shipping through n8n.
The public methodology note shows the proof loop from recommendation click to measurement window: how we earned this recommendation.
The human still approves what ships. The agent does the repeatable work: gather context, preserve memory, cite the source asset, and turn the next action into a reviewable draft.
Example loop
Recommendations end as a ranked list; execution starts as a task file. The agent converts each action into a reviewable artifact - a page edit in Claude Code, a scheduled n8n workflow, a drafted pitch waiting for approval. Foglift's automation templates cover the common loops: weekly visibility reports to Slack or email, competitor score alerts, and scan-triggered tickets.
One recipe replaces the whole reporting loop. weekly-brand-health-report returns tier movement, prompt movement, and competitor deltas in a single call - the digest an agent reads before deciding the week's next move.
// Agent pulls the weekly digest in one call
const report = await foglift.recipeRun({
name: "weekly-brand-health-report"
});
// MCP: recipe_run
// Returns: prompt movement, competitor mention
// deltas, and the recommended next actionGet Foglift running in your AI agent in under 60 seconds.
Best for Claude Code, Cursor, and MCP-compatible agents.
npm install -g foglift-mcpAdd to your MCP config:
{
"mcpServers": {
"foglift": {
"command": "npx",
"args": ["foglift-mcp"]
}
}
}Works with any agent that can make HTTP requests.
curl "https://foglift.io/api/v1/scan\
?url=https://example.com"Or in your agent's code:
const r = await fetch(
"https://foglift.io/api/v1/scan" +
"?url=https://example.com"
);
const data = await r.json();Every aspect of Foglift's output is structured for AI agents to parse, remember, and act on.
Clean, typed responses with consistent schemas. No dashboard scraping required.
Historical AI visibility results, cited URLs, competitors, and sentiment are queryable by an agent.
Recommendations explain which page, prompt, or competitor gap deserves attention next.
Numeric scores and result history give objective before-and-after measurement.
Named, one-call reports like the weekly brand health digest return an entire analysis loop as structured output.
These guides show how founders, marketers, and developers use Foglift data inside weekly agent workflows.
Visibility, recommendation, agent edit, repeat as the weekly operating cadence.
How agents query citation, sentiment, and prompt history directly from Foglift.
A practical lens for whether an agent can move from data to action.
The page structures that make agent-written content citable.
A 4-step workflow for finding why competitors appear when you do not.
The founder schedule for installing, measuring, and compounding visibility.
Foglift publishes machine-readable documentation so AI models can discover and understand our capabilities.
llms.txt
LLM-readable site description at foglift.io/llms.txt
JSON-LD
Schema.org structured data on every page for AI understanding
MCP
Model Context Protocol server on npm
Have an agent inspect prompt gaps, cited sources, and competitor mentions before it drafts the next content brief.
Turn a cited-source gap into a draft pitch that references the correct research asset and waits for human approval.
After an agent edits a page, scan the live URL to catch SEO and AI readiness regressions before the change ships.
Check if a page is ready for AI search. Fix structured data, unblock AI crawlers, add FAQ sections, and rerun the prompt set.
Compare which competitors appear in AI answers, which URLs get cited, and which page format keeps winning.
Agency agents can scan client sites, pull visibility history, generate reports, and propose fixes programmatically.
The agent notices AI referrals flatlining, pulls the citation and source history, and reports why growth stalled - with the ranked fixes attached.
The agent reads which review sites and directories the engines cite for your prompts, flags the ones where you are absent, and drafts the listing copy for human review.
Go beyond one-time checks. AI Visibility Monitor continuously tracks how ChatGPT, Perplexity, Gemini, Claude, and Google AI Overview mention your brand, with visibility trends, citation tracking, and sentiment analysis.
Monitor five AI search engines simultaneously. See which engines mention you and which don't.
Track every time an AI model links to your content. Know your citation rate and which pages get referenced.
See when competitors appear in AI responses where you don't. Identify gaps and opportunities.
Current boundaries for connecting an agent to Foglift data and workflows.
An AI agent can read saved AI Visibility results, answer text, citations, competitor mentions, sentiment, prompt history, Technical Audit results, crawler analytics, referral analytics, and ranked recommendations through supported developer interfaces.
The Connect Assistant supports Claude, ChatGPT, VS Code, Codex, and Gemini. Copilot is shown as unavailable. MCP-compatible clients can also connect through the hosted MCP endpoint.
Developer access begins on Launch at $49 per month. Launch and higher plans include the REST API, CLI, MCP server, API keys, and batch scanning. Free includes the API Playground, AI Traffic Sensor, and up to three webhook endpoints.
Foglift does not publish a page or send outreach from monitoring evidence. It exposes the evidence and ranked recommendation. A connected agent can draft work inside its authorized environment, with human review before an external action.
Let the agent read the same visibility data, citation history, and recommendations that drive the human growth workflow.