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Scan a website for agent readiness

scan_website
Read-onlyIdempotent

Scan a public website and report how ready it is for AI agents and AI search: which checks pass, which are missing, and what each check is, in plain English and technically. Covers 35 checks in three groups: agent readiness (robots.txt and AI crawler rules, sitemap, Link headers, DNS-AID, markdown for agents, Content Signals, API catalog, OAuth discovery, auth.md, MCP server card, A2A agent card, agent skills, WebMCP, ARD), agentic commerce (x402, MPP, UCP, ACP, AP2; scored only for sites that sell online), and AI search foundations (HTTPS, AI crawler access, indexability, content without JavaScript, heading structure, title and description, canonical URL, structured data, business identity markup, language, response time). Use when someone asks whether their website is ready for AI agents, AI search, ChatGPT, Claude, Perplexity, Muse or agentic commerce. A scan takes 5 to 30 seconds.

Input Schema

NameRequiredDescriptionDefault
urlYesThe website to scan, e.g. "example.com" or "https://www.example.com".
detailNo"standard": findings with plain-English and technical explanations. "full": adds pass criteria, request-by-request evidence and source links.standard
sells_onlineNoWhether the site sells online. "auto" detects it from the homepage; use "yes" or "no" when the owner has said.auto

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The description adds genuinely new context: 5-30 second latency, the scoring rule that commerce checks are only scored for sites that sell online, and the 35-check scope. It does not note rate limits or whether the scan is cached, hence not a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded: the first sentence states the action and the report shape, with the check lists and the when-to-use clause following. The exhaustive enumeration of 35 checks is dense, but it earns its place by letting an agent judge coverage before invoking. Slightly heavy, but not padded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the return-shape burden and does so: it says the response reports which checks pass, which are missing, and explains each check, plus latency and the conditional scoring rule. It lacks detail on result size or any truncation behavior, which keeps it short of 5.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so url, detail and sells_online are already fully documented, making 3 the baseline. The description reinforces parameter meaning indirectly ('in plain English and technically' maps to detail; 'sites that sell online' maps to sells_online) but adds no syntax or format detail beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (scan) and resource (public website) plus the exact output intent: readiness for AI agents and AI search, with pass/fail/missing checks. It enumerates the three check groups and the concrete signals covered, so an agent knows precisely what this does. The sibling explain_check is not named explicitly, but the scope difference is inferable from the 'report' framing here.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit when: 'Use when someone asks whether their website is ready for AI agents, AI search, ChatGPT, Claude, Perplexity...'. It names likely trigger phrasings and the agentic-commerce case. It does not name the sibling explain_check or state when NOT to use this versus drilling into a single check, so it stops short of full routing guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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