# AEO Scanner MCP server

AI visibility for ChatGPT/Perplexity/Claude — triple score (AEO+GEO+Agent) with fix code. Free.

## Links
- Registry page: https://www.getdrio.com/mcp/io-github-convrgent-aeo-scanner
- Repository: https://github.com/Convrgent/aeo-scanner-mcp

## Install
- Command: `uvx aeo-scanner`
- Endpoint: https://aeo-mcp.convrgent.ai/mcp
- Auth: Not captured

## Setup notes
- Package: Pypi aeo-scanner v2.2.1
- Environment variable: AEO_API_KEY (secret)
- Remote endpoint: https://aeo-mcp.convrgent.ai/mcp

## Tools
- scan_site - Quick AI visibility scan. Returns three scores: AEO Score (0-100, AI search engine findability), GEO Score (0-100, AI citation readiness), and Agent Readiness Score (0-100, AI agent interaction capability). Also returns AI Identity Card with mention readiness (0-100, predicts how likely AI will mention the brand), detected competitors, business profile (commerce/saas/media/general), and top 5 issues. 67+ checks across 12 categories. Free — no API key needed. Does NOT return per-check details or fix code — use audit_site for full breakdown, fix_site for generated fixes, compare_sites to benchmark against a competitor. Endpoint: https://aeo-mcp.convrgent.ai/mcp
- audit_site - Full AI visibility audit across 67+ checks in 12 categories (4 AEO + 4 GEO + 4 Agent Readiness). Returns detailed per-check scores with specific issues and recommendations, AI Identity Card with mention readiness and detected competitors, and business profile. GEO checks include 3 research-backed citation signals: factual density, answer frontloading, and source citations. Agent Readiness covers emerging agent-discovery standards Cloudflare's isitagentready.com evaluates: RFC 9727 api-catalog, SEP-1649 MCP Server Card, and IETF Content-Signal (draft-romm-aipref). Does NOT generate fix code — use fix_site for that, or compare_sites to benchmark against a competitor. Pay per call ($1.00) via x402 — USDC on Base or Solana. Machine payment via signed X-PAYMENT header; see https://www.x402.org/. On payment_required, the response includes the full x402 payload with payTo/amount/asset. Endpoint: https://aeo-mcp.convrgent.ai/mcp
- fix_site - Generate complete fix code for all AI visibility issues across AEO, GEO, and Agent Readiness. Returns working code you can apply directly — schema generation, robots.txt, sitemap, llms.txt, meta tags, structured data, citation signals, entity markup. Also returns two-tier score projections: quick wins (critical + high fixes only) and full implementation ceiling (all fixes). Content recommendations include research citations. Run scan_site first to see which issues exist. Pay per call ($5.00) via x402 — USDC on Base or Solana. On payment_required, the response includes the full x402 payload with payTo/amount/asset. Endpoint: https://aeo-mcp.convrgent.ai/mcp
- compare_sites - Competitive gap analysis — scans two sites concurrently, shows side-by-side scores, category-by-category winners, competitive gaps (checks where the competitor scored 20+ higher), and generated overtake fix code with projected scores after closing gaps. Use this when the user wants to benchmark against a competitor or when scan_site detects competitors in the AI Identity Card. Pay per call ($3.00) via x402 — USDC on Base or Solana. On payment_required, the response includes the full x402 payload with payTo/amount/asset. Endpoint: https://aeo-mcp.convrgent.ai/mcp

## Resources
- aeo://reference/scoring-methodology - How AEO, GEO, and Agent Readiness scores are calculated across 58+ checks in 12 categories. Includes mention readiness, business profiles, competitive analysis, and research sources.

## Prompts
- optimize_site - Full scan → audit → compare → fix → verify workflow for optimizing a site's AI visibility across AEO, GEO, and Agent Readiness. Arguments: url, priority

## Metadata
- Owner: io.github.Convrgent
- Version: 2.2.1
- Runtime: Pypi
- Transports: STDIO, HTTP
- License: Not captured
- Language: Not captured
- Stars: Not captured
- Updated: Apr 17, 2026
- Source: https://registry.modelcontextprotocol.io