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eclipse-agentready

mcp-name: io.github.CYAN-ECLIPSE-TECHNOLOGIES-LLC/agent-ready

What this is: eclipse-agentready audits storefronts — public e-commerce sites — for AI shopping-agent readiness: can an agent (ChatGPT, Claude, Perplexity, Gemini…) discover the store, read its catalog in machine-readable form, and get correct price/stock data?

What this is not: it does not audit repositories, and it does not grade MCP servers. (The unrelated PyPI project named agentready scores code repositories for AI-assisted development — a different tool for a different job. This one is the CLI edition of Cyan Eclipse Technologies' AgentReady storefront auditor, also available as an MCP server.)

Use — a CI step for merchants and storefront DevOps

$ pip install eclipse-agentready
$ eclipse-agentready audit https://shop.example --fail-under 60
$ eclipse-agentready audit https://shop.example --format json --out report.json
$ eclipse-agentready list-checks

Exit codes: 0 = audit completed and (if --fail-under N given) the score is at least N · 1 = score below N · 2 = the audit itself could not run.

The 8 checks (weights sum to 100)

Check Weight
Live MCP endpoint answers JSON-RPC 20
Product structured data (JSON-LD with price + availability) 20
UCP profile published (/.well-known/ucp) 15
HTTPS + canonical sanity 12
robots.txt allows AI crawlers 10
Sitemap present with product URLs 10
llms.txt present 8
Platform identified 5

Scoring: pass = full weight, warn = half, fail = 0, n/a = excluded. Grade: A ≥ 90 · B ≥ 75 · C ≥ 60 · D ≥ 40 · F < 40. Every check reports observed evidence only, plus a concrete fix hint when it does not fully pass. The CLI and the MCP server run the identical check catalog and scoring path — same evidence in, same grade out.

Honest scope

  • Public-surface audit only. All checks read public endpoints with no credentials, a declared user agent, 10s timeouts, and redirect following.
  • No authenticated checkout execution — this tool never logs in, never buys, and cannot verify a full agent purchase. A store can score well here and still fail at the checkout step (and vice versa).
  • No private data is collected or stored; audit output is the caller's. Evidence is a point-in-time observation; storefronts change.
  • A grade is not an endorsement and not a Bazaar listing claim.

The merchant's path to agent-commerce readiness starts with a grade you can watch: run it in CI on every deploy. A hosted monitoring tier (scheduled re-audits, score-change alerts) is on the product roadmap and is not yet launched.

Powered by Cyan Eclipse Technologies LLC (cetech.ai) — a Veteran-Owned Small Business. MIT licensed. Not affiliated with Shopify, WooCommerce, Automattic, or any audited store; platform names describe detection results only.

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