seo-geo-mcp-server

AI Crawler Access Check

ai_crawler_access

Read-onlyIdempotent

Check which AI/LLM crawlers (GPTBot, ClaudeBot, Google-Extended, Perplexity, etc.) can access a URL by resolving their tokens against robots.txt, distinguishing enforceable blocks from advisory ones and identifying citation-critical bots.

Instructions

Resolve every known AI/LLM crawler against a site's robots.txt and report which may fetch a given path. Covers OpenAI (GPTBot, OAI-SearchBot, ChatGPT-User, OAI-AdsBot), Anthropic (ClaudeBot, Claude-User, Claude-SearchBot), Google (Google-Extended, Googlebot, Google-CloudVertexBot), Perplexity, Apple, Meta, Amazon, Mistral, Common Crawl, ByteDance and others.

Three things this gets right that a naive robots.txt reader does not:

Training vs citation. Blocking GPTBot stops training; blocking OAI-SearchBot stops you being cited in ChatGPT search. Most people want the first, not the second. Blocked citation-critical bots are called out separately.
Which blocks are actually enforceable. Perplexity-User, ChatGPT-User and meta-externalfetcher are documented by their own vendors as ignoring or possibly ignoring robots.txt. A "blocked" verdict for those is advisory, and is reported as such rather than as a clean block.
Vendor quirks. Apple documents that when robots.txt has no Applebot group but does have a Googlebot group, Applebot follows the Googlebot rules — so the effective verdict differs from the literal one.

Each crawler also carries its provenance: whether the token comes from first-party vendor documentation or only from community aggregators. Vendors that publish no crawler token at all (xAI/Grok, Microsoft Copilot) are listed separately, because absence of a rule cannot be read as allowed or blocked.

Args:

site (string): domain or any URL on it.
path (string): path to test (default '/').
include_deprecated (boolean): include retired tokens (default false).
response_format ('markdown' | 'json'): output format (default 'markdown').

Returns: { crawlers[{token, vendor, purpose, allowed, via_wildcard, matched_rule, respects_robots_txt, compliance_note, provenance, quirk}], allowed_count, blocked_count, blocked_citation_critical[], unenforceable_blocks[], undocumented_vendors[], findings[] }.

Example: "Can ChatGPT and Perplexity crawl example.com?" -> ai_crawler_access(site="example.com").

Input Schema

TableJSON Schema

Name	Required	Description	Default
`path`	No	Path to test the rules against, e.g. '/blog/post'. Defaults to '/'.	/
`site`	Yes	Domain or any URL on it, e.g. 'example.com'.
`response_format`	No	Output format: 'markdown' for a human-readable summary (default) or 'json' for the full structured payload.	markdown
`include_deprecated`	No	Also resolve retired tokens (anthropic-ai, claude-web) for historical coverage.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`url`	Yes
`path`	Yes
`crawlers`	Yes
`findings`	Yes
`robots_found`	Yes
`allowed_count`	Yes
`blocked_count`	Yes
`undocumented_vendors`	Yes
`unenforceable_blocks`	Yes
`blocked_citation_critical`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedv1.2.0Sep 16, 2026

changedInput schema / $schema

Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"

removedInput schema / additionalProperties
```
Removed value: -false
```
addedInput schema / properties / path / maxLength
```
Added value: +2048
```

changedOutput schema / $schema

Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"

First observedv1.0.0Aug 3, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Despite annotations already declaring read-only and idempotent behavior, the description adds substantial interpretive context: which blocks are enforceable, vendor-documented provenance, vendor quirks, and the caveat that absence of a rule is not a verdict. This is beyond what the annotations or schema convey.

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?

The description is long but well structured: purpose, differentiators, Args, Returns, and example. The Args section is somewhat redundant with the schema, but the overall organization and front-loaded purpose keep it usable given the tool's complexity.

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?

For a one-required-parameter tool with moderate complexity, this description is complete: coverage list, special cases, return shape, output formats, defaults, and an example. Nothing an agent needs to decide on or invoke the tool 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?

Schema coverage is 100%, and the description's Args section mostly restates the schema's names, defaults, and enum values. It adds a helpful example but does not materially extend the parameter semantics beyond what the input schema already provides.

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?

The first sentence states a specific verb and resource: resolving every known AI/LLM crawler against a site's robots.txt to see what may fetch a path. It also distinguishes the tool from a naive robots.txt reader by naming concrete crawler coverage and special-case behaviors.

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?

The description gives clear context: use this for AI/LLM crawlability questions, with an explicit example and useful distinctions like training vs. citation blocking. It does not explicitly name a sibling alternative or state when to prefer a different tool, so it stops short of a 5.

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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