forgemeshlabs/aso-score-mcp

**What's your ASO score?**
SEO made you visible to search engines. **ASO (Agent Signal Optimization)** makes you discoverable, trustable, and payable by the AI agents that are becoming the web's next visitors.
aso-score-mcp is the free, open-source **ASO Score MCP** — an MCP server that scans any website and produces an **ASO Score Report** scored on the open ASO framework. The beta npm package is @forgemeshlabs/aso-score-mcp.
This release tracks Google's current agent-readiness guidance without overstating it: Google Search says traditional SEO fundamentals still apply to generative AI search, llms.txt is ignored by Google Search itself, and browser agents benefit from clean DOM, screenshot, and accessibility-tree signals. The scanner keeps llms.txt because non-Google agents use it, and adds a browser-agent UX check for semantic controls, linked labels, ARIA/role fallbacks, and hidden-overlay risk.
> **Beta.** Experimental ASO scanner for evaluating whether agents can discover, trust, understand, and use a website/API/tool. ASO scoring is experimental and will evolve as agent standards mature.
What it checks — 34 signals across 6 pillars
Find gaps in **discovery, trust, interoperability, and commerce** — every emerging agent standard in one scan:
| Pillar / Category | Checks |
|---|---|
| **Discovery** | robots.txt, sitemap.xml, Link headers, DNS-AID (_agent. ), /.well-known/ai |
| **Content** | Markdown content negotiation, llms.txt, LLM-readable docs (/index.md, llms-full.txt) |
| **Bot Access** | Explicit AI crawler rules (GPTBot, ClaudeBot, Google-Extended, PerplexityBot…), Content Signals, Web Bot Auth |
| **Interoperability** | API Catalog (RFC 9727), OAuth discovery (RFC 8414), OAuth Protected Resource (RFC 9728), auth.md, **MCP Server Card** (/.well-known/mcp/server-card.json), **Google A2A Agent Card** (/.well-known/agent-card.json, required fields validated), Agent Skills, WebMCP |
| **Commerce** | x402, MPP, UCP, ACP, machine-readable pricing |
| **Identity & Trust** | HTTPS enforcement, JSON-LD/schema.org, agent-friendly browser UX, OpenAPI, agent.json, security.txt, status endpoint, versioning, cross-file identity & signal consistency |
Every check returns **pass / partial / fail** with concrete evidence and a fix recommendation. Results roll up into the six ASO pillars (Discoverability 20, Identity 20, Trust 15, Commerce 15, Reputation 15, Memory 15) → your **ASO Score** and maturity level.
Install
Requires Node.js ≥ 20. Published on npm as @forgemeshlabs/aso-score-mcp — no clone or build needed.
Or skip the install entirely and run it with npx (recommended for MCP clients):
Claude Code
Claude Desktop / Cursor / Windsurf (any MCP client)
Development (from source)
Only needed if you're hacking on the scanner itself:
Tools
| Tool | What it does |
|---|---|
| scan_site | Full ASO scan → ASO Score Report: ASO Score, level, pillar breakdown, all 34 checks with evidence + recommendations |
| get_fix_plan | Prioritized remediation plan with ready-to-paste templates (robots.txt AI rules, llms.txt, agent.json, A2A agent card, MCP server card, x402 manifest, pricing.json, security.txt, status endpoint) |
| check_signal | Run one specific check (e.g. a2a-agent-card, llms-txt, x402) |
| list_checks | Catalog of every check with spec links |
| get_aso_framework | The ASO rubric: pillars, weights, levels, certification thresholds |
Use scan_site for a full baseline, check_signal for a single named signal, get_fix_plan for copy-paste remediation, list_checks to discover valid signal IDs, and get_aso_framework to explain the scoring model without scanning a site.
Try it: *"Scan example.com for ASO score"* · *"What's my ASO score?"* · *"Give me a fix plan to make my site agent-ready."*