Learn: guides, data and tools

The free test reads a store’s public pages the way an AI shopping agent does and scores what it finds. These pages explain what agent readiness means, how each check works, the formula behind every number and how Specoria differs from other kinds of tools.

• What is agent readiness?: The term in plain words: how well agents can reach, read and use a store.
• How the free test works: What the free test requests, how the score is built and what it cannot see.
• Every check, one page each: One page per check: why it matters, how we measure it and how to fix it.
• Glossary and formulas: Definitions and formulas behind the numbers in the test and the panel.
• Specoria and other tools: What each kind of tool does, where Specoria fits and when another kind fits better.
• Readiness specification (SC 1.2): The open specification: every scored check with a stable ID, test and weight.

Most gaps the test finds are fixed in the store platform’s settings or with a few lines of markup. The guides are organized by platform, by product category and by protocol, with separate notes for stores selling in Türkiye and for B2B sellers and brands.

• Platform fix guides: Where to make each fix on common store platforms.
• Online stores by category: The questions shoppers ask agents in each product category and the checks that answer them.
• Agent protocol map: What UCP, ACP, AP2, MCP, WebMCP, llms.txt and related protocols are for and where they stand.
• AI assistant vs agent-ready store: Why an on-site AI assistant and an agent-ready store solve different problems.
• Selling in Türkiye: legal pages agents read: Legal page links, VAT-inclusive prices and the return window on stores in Türkiye.
• Shipping details in Türkiye: what agents read: Shipping cost, free-shipping threshold, delivery time and tracking as agents read them.
• B2B stores and AI agents: what we measure: Case and wholesale terms, price on request and login walls: what stops an agent from buying.
• Product data for manufacturers and brands: How agents tell your product from a look-alike: identifiers, brand and product data.

Our articles and reports use measurements we ran ourselves and public statistics with their sources. Every external figure links to its publisher, and our own figures state how they were measured and on how many stores.

• Insights: Field notes and analysis on agentic commerce, each with its sources.
• Agent Readiness Index 2027: How ready online stores were when we measured them, by level, sector, platform and check.
• Agentic commerce statistics: Sourced, dated figures on AI shopping, each linked to its publisher.
• Sample report: A complete example of the deep audit report.

Free tools that run in your browser, documentation for developers, and the pages where we record what changed, what we got wrong and what comes next.

• Free tools: Browser-only tools for robots.txt, AI bots and agent-readable files. No sign-up.
• API, MCP and GitHub Action: Run the test from scripts, CI or an AI assistant, and read project tasks over MCP.
• Frequently asked questions: Short answers about access, results, pricing and the simulator, each linked to its page.
• Roadmap: What is live, what is built but not open yet, and what is planned.
• What’s new: Every customer-visible change, dated.
• Corrections log: What we published that was wrong, what we changed and when.
• Trust center: Which data we process, who processes it for us and where we use AI.
• Site map: Every public page, grouped by section.