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Agentaeo MCP Server

Audits brand citations across four AI engines and produces revenue estimates, fixes, schema, and llms.txt output.

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Install Agentaeo MCP Server

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "agentaeo-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "@agentaeo/mcp-server@latest"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

We couldn’t confirm it starts correctly

The install command installed and started the server, which then stopped for configuration (such as an API key) that our sandbox does not have — expected for servers that connect to your account.

npx -y @agentaeo/mcp-server@latest

No response to initialize.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 7d ago.

Overview

The agentaeo-mcp-server MCP server runs buyer-query audits across ChatGPT, Perplexity, Claude, and Google AI to measure brand citations. It reports citation rates, competing brands, estimated monthly revenue leakage, and reasons a brand may be omitted. Use it for competitive research, brand monitoring, or an AEO remediation plan inside an MCP-compatible client. Install it with npx and prompt Claude Desktop or another supported client with a domain and query category.

Use cases

  • Audit a domain for citations across four AI engines
  • Compare competitor recommendations for buyer queries
  • Estimate monthly revenue leakage from citation gaps
  • Generate a 30-day AEO remediation plan
  • Create JSON-LD and llms.txt implementation files

Key features

  • Cross-engine AI citation testing
  • Competitor citation analysis
  • Revenue leakage estimates
  • 30-day fix blueprint generation
  • JSON-LD schema generation
  • llms.txt generation

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked Today

Recent health check succeeded.

9/9 checks healthy over the last 45d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 4d ago via OSV.dev · @agentaeo/mcp-server@latest (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

npm downloads1.1kPackage downloads in the last 30 days.
Last commit5mo agoMost recent push to the default branch.
Install checkRunsOur sandbox started it; it then asked for an API key or config.
Directory activity3 viewsConfig copies, upvotes, and views on AllMCPs.

How Agentaeo MCP Server works

What agentaeo-mcp-server MCP server does

The agentaeo-mcp-server MCP server evaluates how often a brand appears in AI-generated answers for buyer-oriented queries. It tests queries across ChatGPT, Perplexity, Claude, and Google AI, then organizes the results by engine and query.

The output is intended to show the gap between being discoverable and being selected. Reports can identify which competitor is cited instead, estimate potential monthly revenue leakage, and explain structural reasons an AI system may skip the audited brand. The server also generates suggested remediation material, including a prioritized 30-day plan, JSON-LD schema, and an llms.txt file.

How it works

Provide a domain and a category or set of buyer queries through an MCP-compatible agent. The server runs live queries against the supported AI engines and returns citation results for each query. Results include an engine-specific citation grade or rate, the competitor that wins a query, and an explanation of the observed citation gap.

The returned information supports several workflows. A company can check its own citation health, compare multiple domains, or investigate why another brand is recommended for a particular category. Agencies can use the results to rank domains by Retrieval Marketing Score and identify accounts with larger gaps.

Setup and configuration

Run the package with:

Terminal
npx @agentaeo/mcp-server@latest

The agentaeo-mcp-server MCP server is also described as usable with Cursor and other MCP-compatible agents.

Tools and capabilities

The agentaeo-mcp-server MCP server supports these documented outputs and workflows:

  • Test buyer queries across ChatGPT, Perplexity, Claude, and Google AI.
  • Report citation rates for each engine and query.
  • Identify which competitor is cited instead of the audited brand.
  • Estimate monthly revenue leakage associated with citation gaps.
  • Explain structural reasons a brand is not selected.
  • Produce a prioritized 30-day remediation blueprint.
  • Generate copy-ready JSON-LD for FAQPage, HowTo, and Organization content.
  • Generate an llms.txt file for publication.
  • Create an executive-level summary of revenue at risk.
  • Compare several domains and rank them by Retrieval Marketing Score.

The generated material is positioned as implementation input rather than as a replacement for reviewing the underlying query results. For example, a team can use the reported competitor differences to decide which schema or content changes to investigate first.

The repository identifies the package as MIT licensed and publishes it as @agentaeo/mcp-server on npm.

Read the full README View source on GitHub

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Frequently Asked Questions about Agentaeo MCP Server

agentaeo-mcp-server is an MCP server that connects AI agents to live citation audits across ChatGPT, Perplexity, Claude, and Google AI. Its main tools measure citation rates, identify competing brands, estimate monthly revenue leakage, explain citation gaps, and generate remediation outputs such as JSON-LD schema and llms.txt files.

Install it by running `npx @agentaeo/mcp-server@latest`. After it starts, configure it for an MCP-compatible client such as Claude Desktop and ask for an audit of a domain and query category.

Yes, the README names Cursor as a supported MCP-compatible agent. You can use it to request citation audits, competitor analysis, and generated remediation outputs, provided the server is configured through Cursor's MCP setup.

It audits ChatGPT, Perplexity, Claude, and Google AI. The results include citation rates by engine and query, the competing brand selected for a query, and information about the audited brand's citation gap.

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