Skip to main content
Glama
fairquery

fairquery-web

Official
by fairquery
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

AI crawler access: robots.txt AI-bot rules + llms.txt (free)

web_ai_crawler_access
Read-onlyIdempotent

Check if AI crawlers like GPTBot or ClaudeBot may access a site via robots.txt, showing per-bot allowed, partial, or blocked status, and detect llms.txt/llms-full.txt.

Instructions

Report whether AI crawlers may access a site, from its robots.txt: per bot "allowed" (no Disallow), "partial" (some paths disallowed) or "blocked" (Disallow: /). Default bots: GPTBot, ChatGPT-User, OAI-SearchBot, ClaudeBot, Claude-User, anthropic-ai, PerplexityBot, Google-Extended, CCBot, Bytespider, Applebot-Extended, Meta-ExternalAgent, Amazonbot. Also checks /llms.txt and /llms-full.txt (llmstxt.org): found, HTTP status, bytes, H1 title, link count (HTML soft-404s are not counted), and lists Sitemap: lines. WHEN TO USE: "can ChatGPT/Claude/Perplexity crawl this site?", GEO/AI-visibility checks, llms.txt adoption. Uses only the origin of the URL. Fetching: read-only GETs from this server with User-Agent "FairqueryBot ... Fairquery-MCP", 15 s timeout per request, body capped (1.5 MB HTML), max 5 redirects; URLs resolving to private/loopback/link-local addresses are refused (SSRF guard). Free, no token, no JavaScript rendering. 3 requests per site. RETURNS: one "ai_crawler_access" object: origin, robots_txt_url, robots_txt_found, robots_txt_status, ai_bots{bot: status}, allowed_count, partial_count, blocked_count, note, sitemaps_declared[], llms_txt{url,found,status,bytes,title,links}, llms_full_txt{...}, checked_at. Errors (isError=true, JSON {error, message, retryable}): INVALID_URL, BLOCKED_PRIVATE_ADDRESS, DNS_FAILED, FETCH_FAILED, TIMEOUT, TOO_MANY_REDIRECTS. WHEN NOT TO USE: for full page SEO use web_seo_audit; for sitemap contents use web_sitemap_info; for many sites use web_batch_audit (paid). COST: free - runs locally in this MCP server (direct HTTP GETs), no APIFY_TOKEN, no Apify billing. EXAMPLE: {"url":"nytimes.com","bots":["GPTBot","ClaudeBot"]} -> {ai_bots:{GPTBot:"allowed|partial|blocked", ClaudeBot:...}, blocked_count, llms_txt, ...}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesRequired. One public domain or http(s) URL, e.g. "vercel.com" or "https://www.python.org/about/". Bare domains are fetched over https; private/loopback hosts are refused.
botsNoUser-agent tokens to evaluate instead of the default AI-bot list, e.g. ["GPTBot","Googlebot"]

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.2.1

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), yet the description adds substantial context beyond them: User-Agent string, 15s per-request timeout, 1.5 MB body cap, 5-redirect limit, SSRF guard on private/loopback/link-local addresses, request count per site, and no JS rendering. It also enumerates the error taxonomy with a JSON error shape.

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 with the core question answered, then organized under clear WHEN TO USE / RETURNS / WHEN NOT TO USE headers, and it ends with a concrete example. There is mild redundancy, e.g. 'Free, no token' restated in the COST line, but it is dense rather than padded.

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?

With no output schema, the description carries the full burden and does so: it enumerates every field of the returned object, the two llms.txt sub-objects, and the complete error set. Nothing an agent needs to call and interpret the tool is missing.

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 both parameters are already documented, but the description adds the concrete default bot list (GPTBot, ClaudeBot, etc.) and clarifies that only the URL origin is used, which the schema does not state. This goes beyond the baseline 3 without fully defining format nuances.

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 ('Report whether AI crawlers may access a site, from its robots.txt') and immediately defines the output vocabulary (allowed/partial/blocked). It also explicitly names sibling tools for adjacent jobs, so an agent can distinguish it without opening a schema.

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?

Gives a dedicated WHEN TO USE ('can ChatGPT/Claude/Perplexity crawl this site?', GEO checks, llms.txt adoption) and WHEN NOT TO USE that routes to three specific siblings (web_seo_audit, web_sitemap_info, web_batch_audit). Both inclusion and exclusion conditions are explicit.

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

Deploy Server

Other Tools