Mcp Agent Accessibility Auditor

com.mambabuiltPublished by mambabuilt.comv1.3.1Updated Nov 7, 2026

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

VerifiedSTDIODesktop onlyWeb Search & ScrapingDeveloper Tools

Overview

AI-generated overview

Audits whether AI agents can read a website, checking llms.txt, robots AI crawler rules, structured data, render mode and machine-readable endpoints.

What you can do
  • Audit one domain's agent readability and get a flat row of 42 fields
  • Check llms.txt, llms-full.txt and ai.txt presence and validity
  • Inspect robots.txt AI crawler policy, including Content Signal directives
  • Check structured data: JSON-LD, microdata, Open Graph and canonical
  • Probe machine-readable endpoints such as sitemap, OpenAPI, security.txt and feeds
  • Limit the run to selected checks or cap the cost per run
When to use it
Useful for technical SEO or growth engineers preparing a site for AI crawlers and agent traffic, or agencies that need before-and-after audits across a client list. It returns deterministic facts read from fetches rather than model judgments.
Requirements
Runs locally via npx as a Node.js package over stdio. Requires an Apify account and an APIFY_TOKEN environment variable; the tool calls the Apify actor and consumes Apify credits.
Before you install
Running the server spends money: you are charged per domain analyzed plus an actor start fee, and it consumes Apify credits, so set max_charge_usd if you want a cap. The APIFY_TOKEN secret is passed in the environment. The domain you submit is sent to Apify's actor, and results are cached for 7 days unless skipCache is set.

Installation

In SourceWeft

  1. Open Mcp Agent Accessibility Auditor in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

Agent Accessibility Auditor MCP Server

[Glama score] [MCP Registry] [npm version] [npm downloads] [license] [mcpservers.org]

[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

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

Claude Desktop

json
{  "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.

InputTypeRequiredNotes
domainstringyesOne company domain, for example vercel.com. A full URL is accepted: the protocol and path are stripped. One domain per call.
checksarraynoRun 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_endpointsbooleannoAlias for the checks array: false removes sitemap, openapi, security_txt and feeds. Ignored when checks is set. Default true.
check_structured_databooleannoAlias for the checks array: false removes json_ld, microdata, open_graph and canonical. Ignored when checks is set. Default true.
skipCacheenumnoLeave as false to use the 7 day cache. Set to true to re-audit the domain from scratch. Default false.
max_charge_usdnumbernoOptional 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.

EventPrice (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.

Source

The actor is on the Apify Store. This wrapper is MIT licensed.

Built by Mamba Labs

Source: README.md at commit 9b365e6

Tools

1

audit_agent_accessibility

Audit Agent Accessibility

Checks whether an AI agent can read one website. Returns one flat row of 42 fields across five families: llms.txt (with llms-full.txt and ai.txt), robots.txt AI crawler policy including Content Signal directives, structured data (JSON-LD, microdata, Open Graph, canonical), render mode, and machine readable endpoints (sitemap, OpenAPI, well known files, feeds). Every field is read off a fetch and no model is called, so a repeat audit changes only when the site changes. Twelve requests per domain, typically 2 to 4 seconds. Use it to audit a site before and after AI crawler work, one domain per call. Do not use it to find which AI vendors a company pays for (that is the AI Tooling Detector) or to read page content. Requires an APIFY_TOKEN and consumes Apify credits: charged per domain analyzed plus a small run start fee. Reads public data only and writes nothing.

ParameterschecksdomainrequiredskipCachemax_charge_usdcheck_endpointscheck_structured_data

Version history

5
  1. v1.3.1LatestNov 5, 2026
  2. v1.3.0Oct 16, 2026
  3. v1.2.1Oct 6, 2026
  4. v1.2.0Oct 6, 2026
  5. v1.1.0Sep 16, 2026

Score

90% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 days25 pts
  • Auth declared10 pts

    Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.

  • Tools listedRequired15 pts
  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts