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Lotus — AI Citation Intelligence

Lotus — AI Citation Intelligence

Remote
clicon.appBusiness & Marketing0Updated 13w ago

About Lotus — AI Citation Intelligence

Lotus is a hosted GEO service for teams tracking whether AI-generated answers cite their brand. It measures citations and crawler activity, then supplies prioritized fixes and verifies them against the live site.

Key features

  1. 01Measure brand citations in LLM answers
  2. 02Review crawler activity and Search Console signals
  3. 03Get prioritized fixes and installation-ready code
  4. 04Verify artifacts on the live site
  5. 05Generate shareable reports

Use cases

  1. 01Check whether AI answers cite a brand
  2. 02Find and apply structured data or llms.txt fixes
  3. 03Verify deployed site changes

MCP support

The hosted Lotus MCP server exposes GEO analysis, citation and crawler data, recommended fixes, generated artifacts, and live-site verification to MCP clients. It uses streamable HTTP; analyze_geo works without credentials, while most other tools require an API key.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://lotus.clicon.app/mcp/
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

All endpoints
Streamable HTTPhttps://lotus.clicon.app/mcp/Auth required

Tools

Details

From the project README

Lotus — AI Citation Intelligence (MCP Server)

Lotus is a Generative Engine Optimization (GEO) platform. It measures whether your brand is actually cited as a source inside LLM-generated answers, and hands your coding agent the exact code to fix what is missing.

This repository documents the hosted Lotus MCP server. The server is a managed remote endpoint — there is nothing to install or self-host.

  • Endpoint: https://lotus.clicon.app/mcp/ (trailing slash required)
  • Transport: streamable-http, stateless
  • Registry: app.clicon/lotus
  • Website: https://clicon.app

What it does

Search engines told you when you ranked badly. Generative engines do not. If an assistant hallucinates your product, ignores you, or cites a competitor instead, you lose the business with no signal that it happened.

Lotus closes that loop in three steps:

  1. Measure — real citation measurements across LLM answers for your domain, plus crawler activity and Search Console signals.
  2. Fix — prioritized "quick wins" with ready-to-install code (JSON-LD structured data, llms.txt).
  3. Verify — Lotus fetches your live site and confirms the fix is really there. A fix is not "applied" because an agent said so; it is applied because it was found in production.

Tools

analyze_geo works without credentials, so any agent can evaluate a domain before signing up. Every other tool requires an API key. Tools that take no domain argument resolve the domain from the key itself.

Preview
ToolDescription
analyze_geoGEO diagnosis for any domain: visibility score and estimated revenue at risk
Read (API key)
ToolDescription
get_quick_winsPending fixes, each with title, category, priority, estimated impact and a hash
get_quick_win_codeFull, install-ready code for one quick win, by hash
get_competitor_actionsCompetitor moves classified by AI-discoverability layer
get_artifactRetrieve a generated artifact (json_ld, llms_txt)
get_bot_activityAI crawler activity on your site over the last N days
get_bleed_modelEstimated business lost through the generative channel, with its provenance
verify_installationCheck whether your artifacts are live on your site right now
get_citation_verdictLatest citation score plus the per-query measurements behind it
Write (API key)
ToolDescription
mark_appliedMarks a quick win as applied — after verifying it against your live site
generate_share_linkCreate a shareable report link
regenerate_artifactsRegenerate your artifacts. Rate-limited; paid tiers only

Configuration

Add the server to any MCP-compatible client (Claude Code, Claude Desktop, Cursor, VS Code, or your own agent):

{
  "mcpServers": {
    "lotus": {
      "url": "https://lotus.clicon.app/mcp/",
      "headers": {
        "X-API-Key": "YOUR_LOTUS_API_KEY"
      }
    }
  }
}

The API key is sent as the X-API-Key header — never as a tool argument or a query parameter. Keys are issued during onboarding at clicon.app. Without a key the server still responds to analyze_geo.

Exact config syntax varies slightly per client; the shape above is what most of them expect.

Usage

Talk to your agent in plain language — it picks the tools:

"Run the Lotus GEO analysis on my domain."

"Show me the pending Lotus quick wins."

"Get me the code for the highest-priority one and apply it to this repo."

"I deployed. Mark it as applied in Lotus."

On that last step Lotus fetches your live site and looks for the schema. If it is there, the fix turns green with a verification date. If it is not — because the deploy has not landed, or the snippet went in wrong — Lotus refuses to mark it. No false positives.

Lotus generates and verifies. You deploy. The server never writes to your site or your repository.

Support

Questions or access: https://clicon.app

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.