@genready/mcp-server

Scan a page for AI readiness

analyze_website

Scan a URL to assess readiness for AI search citations and AI agents; get scores and a prioritized fix checklist.

Instructions

Scan one URL for readiness to be cited by AI search engines (ChatGPT, Perplexity, Google AI Overviews) and used by AI agents. Returns scores and the fix checklist: prioritized problems with concrete fixes. Uses one API credit per call, so re-scan only after deploying a change. scope='content' or 'crawlability' runs a narrower, faster scan.

Input Schema

Name	Required	Description	Default
`url`	Yes	Full http(s) URL of the page to scan
`wait`	No	Wait up to about 25s for the result (default). Slower scans, or false, return a report ID to poll with check_status.
`scope`	No	'full' (default), 'content' (content quality only) or 'crawlability' (technical checks only)	full

Output Schema

Name	Required	Description	Default
`url`	No
`scope`	No
`locked`	No
`scores`	No
`status`	No
`reportId`	No
`fixChecklist`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 18, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare openWorldHint, non-idempotent, non-destructive. The description adds valuable context beyond them: it consumes one API credit per call and describes the returned artifact (scores plus prioritized fix checklist), which meaningfully informs invocation decisions.

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

Conciseness5/5

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

Three sentences, front-loaded with purpose, then output, then cost/usage constraints. No filler; every sentence carries distinct information.

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 an output schema present, return values need not be detailed, and the description still summarizes them. It covers cost and scope trade-offs; the only gap is the absence of explicit sibling routing.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds meaning about scope behavior ('content'/'crawlability' run a narrower, faster scan), clarifying the trade-off beyond the schema's enumeration.

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 and resource ('Scan one URL') scoped to AI-readiness, and the singular 'one URL' implicitly contrasts with site-wide siblings like start_site_analysis. An agent can tell what this does without opening the schema.

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

Usage Guidelines3/5

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

Provides a useful cost-driven guideline ('re-scan only after deploying a change') and explains the scope variants, but never names an alternative tool or states when to prefer start_site_analysis or a narrower scope. Usage is implied rather than routed.

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

Deploy Server