Is Agent Accessibility Auditor free?

Yes, Agent Accessibility Auditor is free to use.

How do I install Agent Accessibility Auditor?

Agent Accessibility Auditor is a local plugin. Install it using npm package: @mambalabsdev/mcp-agent-accessibility-auditor and add the generated configuration snippet to your AI app's MCP config file. Then restart your AI app.

Is Agent Accessibility Auditor safe to use?

Yes. Agent Accessibility Auditor passed MCP Marketplace's automated security scan with a score of 10/10 (low risk). Every server on MCP Marketplace is security-scanned before it's listed; see the full security report on this page for the findings and permissions.

What credentials does Agent Accessibility Auditor need?

Agent Accessibility Auditor requires the following credentials or environment variables: APIFY_TOKEN. You can find setup instructions on the server detail page.

What AI apps work with Agent Accessibility Auditor?

Agent Accessibility Auditor uses the Model Context Protocol (MCP) and works with any MCP-compatible AI app, including Claude, ChatGPT / Codex, Gemini, Copilot, Cursor, and more.

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Agent Accessibility Auditor MCP Server

by mambabuilt

Developer ToolsLow Risk10.0Local

Free

Audit whether an AI agent can read a site: llms.txt, robots AI rules, structured data.

About

Audit whether an AI agent can read a site: llms.txt, robots AI rules, structured data.

Security Report

10.0

Low Risk10.0Low Risk

Valid MCP server (2 strong, 3 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry. Trust signals: trusted author (58/58 approved).

10 files analyzed · 1 issue found

Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.

Permissions Required

This plugin requests these system permissions. Most are normal for its category.

file_system

Check that this permission is expected for this type of plugin.

HTTP Network Access

Connects to external APIs or services over the internet.

env_vars

Check that this permission is expected for this type of plugin.

Shell Command Execution

Runs commands on your machine. Be cautious — only use if you trust this plugin.

What You'll Need

Set these up before or after installing:

Apify API token from https://console.apify.com/account/integrationsRequired

Environment variable: APIFY_TOKEN

How to Install

Add this to your MCP configuration file:

{
  "mcpServers": {
    "com-mambabuilt-mcp-agent-accessibility-auditor": {
      "env": {
        "APIFY_TOKEN": "your-apify-token-here"
      },
      "args": [
        "-y",
        "@mambalabsdev/mcp-agent-accessibility-auditor"
      ],
      "command": "npx"
    }
  }
}

Documentation

View on GitHub

From the project's GitHub README.

Agent Accessibility Auditor MCP Server

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.

Install

npx -y @mambalabsdev/mcp-agent-accessibility-auditor

Claude Desktop

{
  "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.

Tool

`audit_agent_accessibility`

Domain in, whether an AI agent can read that site out.

Input	Type	Required	Notes
`domain`	string	yes	One company domain, for example vercel.com. A full URL is accepted: the protocol and path are stripped. One domain per call.
`checks`	array	no	Run only these checks: `llms_txt`, `robots_ai`, `sitemap`, `openapi`, `security_txt`, `feeds`, `json_ld`, `microdata`, `open_graph`, `canonical`, `render_mode`. Omit for all of them. A check you did not run reports `null`, never `false`, and the score is rescaled over what you selected.
`check_endpoints`	boolean	no	Alias for the checks array: false removes `sitemap`, `openapi`, `security_txt` and `feeds`. Ignored when `checks` is set. Default `true`.
`check_structured_data`	boolean	no	Alias for the checks array: false removes `json_ld`, `microdata`, `open_graph` and `canonical`. Ignored when `checks` is set. Default `true`.
`skipCache`	enum	no	Leave as `false` to use the 7 day cache. Set to `true` to re-audit the domain from scratch. Default `false`.
`max_charge_usd`	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.

Reading the output

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.

Billing

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 (`domain-analyzed`)	0.005
Actor Start (`apify-actor-start`)	0.00005

Pricing is on the actor's Apify page. Running this server consumes Apify credits.

What this server does and does not do

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.