Anthropic MCP Goes GA: The Agent Handshake Is Now Standard
Anthropic's Model Context Protocol reached general availability with broad platform adoption this spring, making structured agent-facing endpoints a baseline expectation rather than an experiment. Businesses without MCP-compatible surfaces are now invisible to an expanding class of autonomous agents executing real transactions.
This Week's Signals
MCP General Availability Triggers Wave of Platform Integrations Across SaaS and E-Commerce
Why it matters for your score
With MCP now GA and adopted by Shopify, Cloudflare, and dozens of SaaS platforms, AI agents running on Claude — and increasingly on other models via the open spec — can discover, authenticate, and transact with businesses that expose MCP-compatible endpoints. If your business runs on a platform that has shipped MCP support, your catalog, pricing, and availability data may already be agent-readable — but only if your schema and permissions are correctly configured. Businesses not yet on an MCP-enabled platform need to treat this as an infrastructure gap, not a future consideration.
Perplexity Tightens Local and Vertical Citation Logic, Favoring Structured Business Profiles
Why it matters for your score
Perplexity's answer engine has shifted toward citing businesses with explicit structured data signals — particularly JSON-LD LocalBusiness, Service, and FAQPage markup — over plain-text directory listings when surfacing recommendations in shopping and professional services queries. Businesses with clean schema implementations are appearing in cited answer cards; those relying on third-party profile pages without owned structured data are being displaced. This is the clearest confirmation yet that on-site schema directly influences AI citation frequency, not just traditional search ranking.
llms.txt Adoption Crosses 40,000 Indexed Domains as Crawlers Begin Active Parsing
Why it matters for your score
The llms.txt standard, proposed by Answer.AI's Jeremy Howard, has crossed a meaningful adoption threshold with verified parsing now confirmed from ClaudeBot and at least one Perplexity crawler variant. The file gives AI models a machine-readable summary of who you are, what you offer, and which URLs matter — cutting through the noise of a full site crawl. Businesses that have not yet published an llms.txt file are surrendering a direct, low-cost channel to shape how AI systems summarize and recommend them, especially for long-tail and conversational queries where crawl depth is limited.
What to do this week
- 1
Publish or audit your llms.txt file this week — ClaudeBot and Perplexity crawler variants are confirmed parsing them. Use Faro's /tools/llms-txt generator to build a structured file that includes your core service descriptions, pricing page URL, and a plain-language business summary. Treat it like your AI business card: if it's missing or stale, you're under-represented in model context.
- 2
Run a Faro AI Readiness Scan (/tools/ai-readiness-scan) specifically against your JSON-LD schema to check for LocalBusiness, Service, and FAQPage completeness — Perplexity's updated citation logic is directly rewarding these signals in answer cards. If your schema is thin or missing the 'priceRange', 'areaServed', or 'hasOfferCatalog' properties, use /tools/ai-schema to patch those fields before next week's crawl cycle.
- 3
If your platform (Shopify, any Cloudflare Workers-based stack, or a major SaaS CRM) has shipped MCP support, verify that your product catalog, availability, and pricing endpoints are correctly permissioned for agent access — check your robots.txt isn't blocking MCP-relevant paths using Faro's /tools/robots-txt auditor. If you're on a platform without MCP support yet, flag it as a vendor evaluation criterion for Q3; agent transaction traffic is no longer hypothetical.