fairquery-web

Official

by fairquery

Overview Schema Source Related Servers Discussions Admin

TypeScript

Local

Bulk website tech, SEO & AI-readiness audit (Apify, paid)

web_batch_audit

Read-onlyIdempotent

Bulk audit up to thousands of websites in one call: collect technologies, SEO issues, security headers, AI crawler rules, sitemaps, and readiness scores for lead or portfolio scans.

Instructions

Bulk audit up to thousands of websites in one call via the Fairquery Apify Actor fairquery/website-tech-seo-ai-readiness: technologies, on-page SEO, security headers, robots.txt AI-bot rules, XML sitemap, llms.txt, seo_score and ai_readiness_score per site. WHEN TO USE: lists of 20+ sites (lead enrichment, portfolio/market scans) where you want server-side parallelism and a dataset. For one or a few sites prefer the free tools: web_quick_check, web_seo_audit, web_ai_crawler_access, web_tech_stack, web_sitemap_info, web_security_headers. Rows can be stored and later diffed with web_compare_snapshots. BEHAVIOR: read-only; runs the Actor via the Apify API (run-sync), a few polite GET requests per site, respects robots.txt by default, no JS rendering. Requires APIFY_TOKEN (never logged). Typical 2-10 s per site, parallel. COST: paid (billed to your Apify account, pay-per-event): $0.00005 per run + $0.002 per site row; error rows are free. 50 sites ~ $0.10. RETURNS: {count, items}. site rows: domain, status_code, https, response_ms, title, meta_description, canonical, lang, h1[], word_count, open_graph, json_ld_types, technologies[{name, category, evidence}], security_headers, robots_txt_found, ai_bots{bot: allowed|partial|blocked}, sitemap_found, sitemap_url_count, llms_txt_found, llms_txt_title, seo_score, seo_issues[{code, severity, message}], ai_readiness_score, source_url, checked_at. error rows: code (INPUT_EMPTY, INVALID_URL, MAX_SITES_EXCEEDED, ROBOTS_DISALLOWED, FETCH_FAILED, HTTP_ERROR, BLOCKED_OR_RATE_LIMITED), message, retryable, input. Tool-level errors: APIFY_TOKEN_MISSING, APIFY_ACTOR_RUN_FAILED. WHEN NOT TO USE: for 1-20 sites or interactive questions use the free tools above (same checks, no cost). EXAMPLE: {"urls":["vercel.com","netlify.com"],"checkLlmsTxt":false} -> {count:2, items:[{type:"site", domain:"vercel.com", seo_score:..., ...}, ...]}

Input Schema

TableJSON Schema

Name	Required	Description
`urls`	Yes	Domains or URLs, e.g. ["vercel.com","https://www.python.org/about/"]. Bare domains use https. One row per unique URL.
`maxSites`	No	Safety cap on unique URLs per run (default 500); extras produce a MAX_SITES_EXCEEDED error row
`checkRobots`	No	Fetch robots.txt and report allow/partial/blocked per AI bot (default true)
`checkLlmsTxt`	No	Check /llms.txt and /llms-full.txt (default true)
`checkSitemap`	No	Find the XML sitemap and count URLs (default true)
`respectRobotsTxt`	No	Skip pages robots.txt disallows for generic crawlers, emitting ROBOTS_DISALLOWED (default true, recommended)

Output Schema

TableJSON Schema

Name	Required	Description	Default
`count`	Yes	Number of rows returned
`items`	Yes	Result rows; each has a "type" field (data rows or machine-readable "error" rows)

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.2.1Oct 17, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Goes well beyond the annotations by disclosing cost model ($0.002/site, error rows free), auth requirement (APIFY_TOKEN, never logged), rate/politeness behavior, no-JS rendering, robots.txt respect, and per-site latency, all of which an agent needs before invoking a paid bulk run.

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?

Labeled sections (WHEN TO USE, BEHAVIOR, COST, RETURNS, WHEN NOT TO USE, EXAMPLE) front-load the routing decision, and every line carries actionable information despite the length.

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

Completeness5/5

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

Covers routing, cost, auth, error taxonomy (INPUT_EMPTY through BLOCKED_OR_RATE_LIMITED plus APIFY_TOKEN_MISSING), and even the return shape, leaving nothing an agent needs to call this correctly.

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 the baseline is 3, but the description adds the concrete example invocation (urls + checkLlmsTxt) and ties parameters to outcomes like MAX_SITES_EXCEEDED error rows, giving marginal value above 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 (bulk audit) and resource (websites) with explicit scope ('up to thousands in one call') and names the underlying Actor, making it unmistakable what it does versus the single-site siblings.

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

Usage Guidelines5/5

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

Explicit WHEN TO USE (20+ sites, lead enrichment, portfolio scans) and WHEN NOT TO USE (1-20 sites or interactive questions) naming six alternative free tools by name, plus the snapshot-diff sibling for follow-up.

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

Deploy Server