Audit a domain for AI-agent accessibility and return a flat 42-field fact row.
Run audit_agent_accessibility with one required domain (URL accepted; protocol/path stripped).
Get facts on llms.txt/llms-full.txt/ai.txt, robots.txt AI crawler policy including Content Signal, structured data health (JSON-LD, microdata, Open Graph, canonical), render mode, and machine-readable endpoints (sitemap, OpenAPI, well-known files, feeds).
Toggle endpoint probing with check_endpoints and structured-data parsing with check_structured_data.
Use the 7-day cache by default or set skipCache to true to re-audit.
Requires APIFY_TOKEN and consumes Apify credits; tool is read-only, idempotent, open-world, non-destructive, and calls no model.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Agent Accessibility Auditor MCP Server Can AI agents access and read example.com?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP server for the Mamba Labs Agent Accessibility Auditor actor on Apify.
Can an AI agent read this site? Give it a domain and it returns one flat row of 42 fields covering five families of fact: the llms.txt family, robots AI crawler policy including the newer Content Signal directives, structured data presence and health, render mode, and machine readable endpoint discovery.
npx -y @mambalabsdev/mcp-agent-accessibility-auditor{
"mcpServers": {
"mamba-agent-accessibility-auditor": {
"command": "npx",
"args": ["-y", "@mambalabsdev/mcp-agent-accessibility-auditor"],
"env": { "APIFY_TOKEN": "your-apify-token" }
}
}
}Get an Apify token at console.apify.com/account/integrations.
Related MCP server: maxaeo-ai-visibility-mcp
audit_agent_accessibilityDomain in, whether an AI agent can read that site out.
Input | Type | Required | Notes |
| string | yes | One company domain, for example vercel.com. A full URL is accepted: the protocol and path are stripped. One domain per call. |
| array | no | Run only these checks: |
| boolean | no | Alias for the checks array: false removes |
| boolean | no | Alias for the checks array: false removes |
| enum | no | Leave as |
| number | no | Optional cost cap in USD for this run. The actor stops before an item that could take its charges past this amount. Omit to rely on the maximum cost per run set in Apify. |
Every field is a fact read off a fetch. No model is called at any point, so the same domain returns the same row today and next month unless the site actually changed.
has_llms_txt is true only when /llms.txt returns 200 and the body is real markdown, and llms_txt_reject_reason says why a 200 was not counted. Twelve requests per domain, robots.txt first and then the homepage and ten probes concurrently. Typical wall clock is 2 to 4 seconds.
Built for a technical SEO or growth engineer preparing a site for AI crawlers and agent traffic, or an agency selling that work and needing a before and after audit across a client list.
You are charged per domain analyzed, plus a small actor start fee. A repeat run inside the 7 day cache window costs nothing new.
Price per event on the Apify FREE plan, from the live pricing record; paid Apify plans pay less per event.
Event | Price (USD) |
Domain analyzed ( | 0.005 |
Actor Start ( | 0.00005 |
Pricing is on the actor's Apify page. Running this server consumes Apify credits.
It is a thin client for the Apify actor. It passes your input through and returns the actor's output unchanged. Every behavior described above lives in the actor, not here.
The tool starts the actor run and polls it to a finished status, so a long run is not cut off at 300 seconds. The run is allowed 1,800 seconds. If it is still going two minutes after that, the call stops waiting and returns the run ID with a link to it in the Apify Console, where the results land when it finishes. A run that does not succeed comes back as an error with its run ID and status.
Errors are surfaced, never swallowed. An invalid input, an invalid token, an exhausted balance, a timeout, or a run that returns anything other than a dataset all come back as an explicit tool error rather than as an empty result.
The actor is on the Apify Store. This wrapper is MIT licensed.
Built by Mamba Labs
Give it a domain and it returns whether an AI agent can read that site, and what the site's policy says, as one flat row of 42 fields across five families: the llms.txt family including llms-full.txt and ai.txt, robots.txt AI crawler policy including the newer Content Signal directives, structured data presence and health across JSON-LD, microdata, Open Graph and canonical, render mode, and machine readable endpoint discovery covering sitemap, OpenAPI, well known files and feeds. Every field is a fact read off a fetch. No model is called at any point, so the same domain returns the same row today and next month unless the site actually changed. Twelve requests per domain, typically 2 to 4 seconds. Built for a technical SEO or growth engineer preparing a site for AI crawlers, or an agency selling that work and needing a before and after audit across a client list. Requires an APIFY_TOKEN and consumes Apify credits. Read only.
Inputs: domain (required), skipCache, check_endpoints, check_structured_data.
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Scored across 1 tool
With only one tool, there is no possibility of confusion or overlap. The tool's purpose is clearly defined and distinct by virtue of being the sole member of the set.
The single tool name follows a clear verb_noun pattern (audit_agent_accessibility), consistent with common MCP naming conventions. There are no other tools to conflict with this pattern.
A single tool feels minimal for a server, but the tool itself is highly specialized and performs a comprehensive audit in one action. The count is borderline, as it could benefit from additional tools like listing domains or comparing audits, but the narrow scope partially justifies the thin surface.
For the stated purpose of auditing agent accessibility, the tool covers a wide range of signals (llms.txt, robots.txt, structured data, render mode, endpoint discovery) in a single output. The only gap is the lack of supporting operations, but as a read-only audit tool, the core domain is well covered.
Scan any domain for AI readiness and generate llms.txt, robots.txt and schema. Free, read-only.
Autonomous AI agent readiness, crawler reachability, and 30-point audit specification engine.
Checks llms.txt, AI crawler access in robots.txt, and sitemap - with a 0-100 AI readiness score.
Audit webpage access for AI crawlers. API key and Premium or Partner plan required.