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Ai Crawlers

ai_crawlers
Read-only

For eleven AI crawlers (GPTBot, ClaudeBot, PerplexityBot, Google-Extended and others): allowed or blocked for the page's path under robots.txt, plus the noai and noimageai opt-outs in meta and X-Robots-Tag, and whether llms.txt exists. Reads the page as static HTML; no JavaScript runs. Price: $0.002 in USDC per successful call, paid with x402 (Base, Solana or Arc); failed calls are not charged.

Input Schema

NameRequiredDescriptionDefault
urlYesPage address, https://…

Output Schema

NameRequiredDescriptionDefault
urlNo
pathNo
optOutNo
sourceNo
llmsTxtNo
crawlersNo
checkedAtNo
robotsTxtNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4/5.0
Behavior5/5

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

Annotations only cover readOnlyHint and openWorldHint, and the description adds substantial behavior beyond them: a $0.002 USDC per-call price, x402 payment rails (Base, Solana, Arc), the fact that failed calls are not charged, and the no-JavaScript execution model. Pricing and billing semantics are exactly the kind of information annotations cannot express.

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?

A single dense but well-ordered passage: coverage first, then the execution caveat, then the price. Every clause carries information, though the parenthetical crawler list and payment-rail enumeration make it heavy for one sentence.

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?

An output schema exists, so return values need no explanation; the description still tells the agent the scope (eleven crawlers, with examples), the three data sources checked, the rendering limitation, and the cost. Nothing needed to call it correctly is missing.

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?

One parameter with 100% schema description coverage ('Page address, https://…'), so the schema already carries the full parameter semantics. The description adds nothing about the url beyond confirming the page is fetched as static HTML; baseline 3 is appropriate.

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

Purpose4/5

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

Names the specific resource (eleven named AI crawlers) and the exact outputs: robots.txt allow/block status, noai/noimageai meta and X-Robots-Tag opt-outs, and llms.txt existence. That is far more specific than a tautology, but it never distinguishes itself from overlapping siblings such as robots, llms_txt, meta, or headers, which an agent must disambiguate against.

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?

Usage is only implied: the 'static HTML; no JavaScript runs' caveat tells the agent this fails on JS-rendered pages, which is useful context. However, no when-to-use, when-not-to-use, or named alternative is given, despite several close siblings (robots, llms_txt, meta, headers) that could be confused with it.

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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