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datasets_llms_facets

Facet the AI-readiness (llms.txt) dataset. Returns distribution counts over the AI-readiness census (dataset id enum value llms), honoring the same filters as search — adoption by producer, generator, TLD or category, and which AI…

Instructions

Facet the AI-readiness (llms.txt) dataset. Returns distribution counts over the AI-readiness census (dataset id enum value llms), honoring the same filters as search — adoption by producer, generator, TLD or category, and which AI crawlers robots.txt blocks. Facet enum: state, source_kind, generator, bot, policy, tld, category, robots_state, robots_default, blocked_crawler, partial_crawler, content_signal_train, full_state, home_status. The filter enums are the same as search. Counts include blocked and error probes, which are unknown rather than absent; filter state=present or state=absent to count only sites that were established either way.

Input Schema

NameRequiredDescriptionDefault
qNoSubstring match on the site domain, max 256 characters
botNoAI crawler named inside a valid /llms.txt, e.g. GPTBot (see facet=bot for the values)
tldNoExact top-level-domain filter, e.g. com, org, io
textNoFull-text match on the /llms.txt title and summary, max 256 characters
facetYesFacet enum: state, source_kind, generator, bot, policy, tld, category, robots_state, robots_default, blocked_crawler, partial_crawler, content_signal_train, full_state, home_status
stateNo/llms.txt state. Enum: present, absent, blocked, error, skipped. blocked and error mean unknown, not absent
validNotrue keeps only sites serving a valid /llms.txt, false keeps the rest
run_idNoScan run id; defaults to the latest run
categoryNoExact category label carried by the source list; the top-1M run has none, so this is empty for it
generatorNoGenerator detected in a valid /llms.txt. Enum: yoast, aioseo, rankmath, wix, shopify, parked
injectionNotrue keeps only valid /llms.txt files containing instruction-override text aimed at AI readers
min_linksNoMinimum number of links in a valid /llms.txt, 0 or greater
reachableNotrue keeps only sites whose homepage control request answered, false only sites that did not
blocks_allNoRepeatable AI crawler that robots.txt must block (AND). Enum: GPTBot, OAI-SearchBot, ChatGPT-User, ClaudeBot, Claude-SearchBot, Claude-User, anthropic-ai, Google-Extended, PerplexityBot, Perplexity-User, CCBot, Bytespider, Applebot-Extended, Amazonbot, meta-externalagent, cohere-ai, Diffbot
blocks_anyNoRepeatable AI crawler; robots.txt must block at least one (OR). Enum: GPTBot, OAI-SearchBot, ChatGPT-User, ClaudeBot, Claude-SearchBot, Claude-User, anthropic-ai, Google-Extended, PerplexityBot, Perplexity-User, CCBot, Bytespider, Applebot-Extended, Amazonbot, meta-externalagent, cohere-ai, Diffbot
full_validNotrue keeps only sites serving a valid /llms-full.txt
not_blockedNoRepeatable AI crawler that robots.txt must NOT block. Enum: GPTBot, OAI-SearchBot, ChatGPT-User, ClaudeBot, Claude-SearchBot, Claude-User, anthropic-ai, Google-Extended, PerplexityBot, Perplexity-User, CCBot, Bytespider, Applebot-Extended, Amazonbot, meta-externalagent, cohere-ai, Diffbot
placeholderNotrue keeps only valid /llms.txt files that still hold template placeholder text
source_kindNoProducer of a valid /llms.txt. Enum: shopify_agent_template, seo_plugin, platform_default, parked, custom
home_blockedNotrue keeps only sites whose homepage was walled off from the scan (a block or challenge)
mentions_mcpNotrue keeps only valid /llms.txt files that mention the Model Context Protocol
robots_stateNo/robots.txt state. Enum: present, absent, blocked, error, skipped
all_ai_blockedNotrue keeps only sites whose robots.txt blocks all 17 tracked AI crawlers
robots_defaultNoDefault stance of robots.txt toward AI crawlers. Enum: allowed, partial, blocked
mentions_openapiNotrue keeps only valid /llms.txt files that mention OpenAPI or Swagger
is_infrastructureNofalse (the common case) excludes backend CDN/DNS/cloud-vendor hostnames that rank highly but serve no public homepage; true keeps only those
min_train_blockedNoMinimum number of AI training crawlers (of 11 tracked) that robots.txt blocks, 0 or greater
content_signal_trainNoContent-Signal ai-train value declared in robots.txt. Enum: yes, no

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.17.12

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden and does disclose a genuinely non-obvious behavior: blocked/error probes are counted as 'unknown rather than absent,' with guidance to filter state=present/absent. It does not mention read-only status, rate limits, or pagination, but the key semantic caveat about the counts is surfaced.

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?

Purpose is front-loaded in the first sentence, followed by useful semantic detail. However, the 14-value facet enum is reproduced verbatim from the schema, which is redundant padding that a careful editor would drop.

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?

For a 28-parameter facet/aggregation tool with no output schema, the description explains what is returned (distribution counts) and the important count semantics around blocked/error. It is complete enough to invoke correctly, though it leaves return-object shape and any limits unstated.

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 the schema already documents all 28 params including the facet enum and filter enums. The description re-lists the facet enum and states the filter enums match search, adding light orientation but little beyond the schema; baseline 3 applies.

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 (facet) and resource (the AI-readiness / llms.txt census dataset), and immediately clarifies it returns distribution counts rather than records. It distinguishes itself from the sibling search tool by noting it honors 'the same filters as search,' so an agent can tell aggregation from retrieval without opening either schema.

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?

Clear context: this tool produces distribution counts, and it explicitly tells the agent how to constrain counts ('filter state=present or state=absent'). It references search as the filtering sibling but stops short of an explicit when-to-use-this-vs-search rule, so no hard exclusions are stated.

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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