superaudit-mcp
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., "@superaudit-mcprun a website audit on example.com and summarize the results"
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.
superaudit-mcp
MCP (Model Context Protocol) server that exposes SuperAudit's real, free audit as a tool that any MCP-compatible AI agent can call.
With this, an external developer can ask their own agent (Claude, or another MCP client) for things like "audit this website with SuperAudit" without talking to anyone on the SuperAudit team — the tool calls the public production endpoint directly.
What it does
Exposes a single tool: audit_website.
Input:
url(required): domain or URL to audit, e.g."minegocio.es"or"https://minegocio.es".raw_json(optional,boolean, defaults tofalse): iftrue, in addition to the readable summary it returns the full JSON exactly as SuperAudit delivers it (all modules, all scores, the plans).
What it does internally: calls
POST https://superaudit.airpagents.pro/api/quick-auditwith{ "web": "<url>" }— the same public endpoint SuperAudit's landing page uses today for the free scan. It needs no key or token.Output: a text summary with the global score (0-100), the score for each of the ~27 modules (SEO, security, legal/GDPR, Core Web Vitals, WCAG accessibility, generative AI positioning, WordPress/CVEs, OWASP Top 10, etc.), the main issues detected and the paid plan SuperAudit recommends based on the result.
Related MCP server: foglift-mcp
Known limits (important)
The public endpoint has a limit of 10 requests per minute per IP on the SuperAudit server. If exceeded, the tool returns an error indicating so — it does not retry automatically to avoid overloading the service.
The backend caches each audited domain for 24 hours, so re-auditing the same website shortly after is instant.
A "cold" audit (uncached domain) can easily take several seconds: the client waits up to 90s before considering the attempt failed.
The backend validates that the URL points to a real public domain (blocks private IPs/localhost as an SSRF measure) — if the website doesn't exist or isn't online, the tool returns a readable error, not a silent failure.
Installation (for an external developer)
Requirements: Node.js 18 or higher.
git clone <este repositorio> # o simplemente copia esta carpeta
cd superaudit-mcp
npm install
npm run buildThis generates dist/index.js, which is the MCP server binary (it speaks over
stdio, the standard MCP transport).
Try it standalone (optional)
npm run build
node dist/index.jsThe process stays waiting on stdio — that's normal, that's how MCP servers work. It connects from an MCP client, it's not used as an interactive CLI.
How to connect it to an MCP client
Claude Code
claude mcp add superaudit -- node "/ruta/completa/a/superaudit-mcp/dist/index.js"Claude Desktop
Edit the Claude Desktop configuration file (claude_desktop_config.json)
and add:
{
"mcpServers": {
"superaudit": {
"command": "node",
"args": ["/ruta/completa/a/superaudit-mcp/dist/index.js"]
}
}
}Restart Claude Desktop. The audit_website tool will appear available for
the agent to use when the conversation requires it (e.g. "audit
minegocio.es with SuperAudit").
Any other MCP client
Any client that supports MCP servers over stdio can launch
node dist/index.js as the server command — it requires no additional
configuration or environment variables.
Environment variables (optional)
SUPERAUDIT_BASE_URL: defaults tohttps://superaudit.airpagents.pro. It only makes sense to change it to point to your own test environment.
No API key is needed: the endpoint this tool uses is the same one SuperAudit's public landing page uses for the free scan.
Development
npm install
npm run build # compila TypeScript → dist/There are no automated tests in this initial deliverable — it was verified
manually with a real MCP client (handshake initialize + tools/list
tools/call) against the production endpoint.
Available Tools
1 toolaudit_websiteAudita una web con SuperAuditA
Ejecuta una auditoría real y gratuita de SuperAudit sobre una web: SEO técnico, seguridad, cumplimiento legal (RGPD/LSSI), Core Web Vitals, accesibilidad, posicionamiento en IA generativa (GEO), WordPress/CVEs, y más de 25 módulos en total. Devuelve un score global de 0 a 100, el detalle por módulo y los principales problemas encontrados, priorizados. Útil para responder preguntas como '¿qué falla en la web de mi cliente?' o 'audita esta URL antes de contactarles'. Sujeto a un límite de uso justo (rate limit) en el servidor de SuperAudit.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Dominio o URL a auditar, por ejemplo 'minegocio.es' o 'https://minegocio.es'. | |
| raw_json | No | Si es true, además del resumen legible incluye el JSON completo devuelto por SuperAudit (todos los módulos, scores y planes). Por defecto false para no saturar el contexto. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, and it discloses important behavior: it is a real and free audit, it respects a fair-use rate limit on SuperAudit's server, and it returns a global score, per-module detail, and prioritized problems. It does not state explicit side-effect/safety information, but an audit is clearly presented as a non-mutating analysis, and the rate-limit caveat is a useful limitation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is dense but not bloated: the first sentence delivers the core purpose and scope, followed by the output format, use cases, and rate limit. Information is front-loaded and every clause earns its place, though the first sentence is long.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having no output schema or annotations, the description covers what the tool does, what it returns, when to use it, and an operational constraint (rate limit). Combined with a fully documented input schema, an agent has enough context to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents 'url' and 'raw_json'. The description itself does not add parameter-specific detail; it only contextualizes the overall output. This meets the baseline but does not exceed it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific action and object: 'Ejecuta una auditoría real y gratuita de SuperAudit sobre una web', and enumerates the audit areas (SEO técnico, seguridad, RGPD/LSSI, Core Web Vitals, accesibilidad, GEO, WordPress/CVEs). It also states the concrete return value (score 0-100, module details, prioritized issues). With no sibling tools to disambiguate, the purpose is fully identifiable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives concrete use cases: '¿qué falla en la web de mi cliente?' or 'audita esta URL antes de contactarles'. This makes the intended invocation context clear. There are no explicit exclusions or alternative tool routing, but no siblings are provided, so this is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v1.0.0- First observed
audit_website
TDQS
Scored across 1 tool
Only one tool exists, so there is no ambiguity between tools. The single tool has a clear, distinct purpose around website auditing.
With a single tool, the naming is trivially consistent. 'audit_website' follows a clear verb_noun pattern.
A single tool feels thin and borderline for a server. It consolidates many audit checks into one call, but offers no auxiliary operations like listing or retrieving past audits.
The tool comprehensively covers the core audit workflow, including many modules. Minor gaps exist, such as no ability to fetch historical audits or compare results over time.
Maintenance
Related MCP Connectors
Find, compare, and audit software for AI agents. Scored registry of tools and MCP servers.
Free public MCP for AI agents — 193 tools, 44 workflows. No API key.
- UnifAPIOAuthcom.unifapi
Hosted MCP server for live public-data APIs and Skills for AI agents.
- RampifyOAuthdev.rampify
SEO MCP server: crawl your site, find AI-visibility gaps, and ship the fix from your coding agent.
Related MCP Servers
- AlicenseBqualityBmaintenanceMCP server that enables AI agents to perform comprehensive web audits using Google Lighthouse with 13+ tools for performance, accessibility, SEO, and security analysis.11994 npm70MIT
- AlicenseNot gradedqualityNot gradedmaintenanceMCP server for website SEO + GEO analysis. Scan any URL to get scores across 5 categories (SEO, GEO, Performance, Security, Accessibility) with actionable fix recommendations. Enables AI coding assistants to audit websites and implement fixes autonomously.-
- AlicenseAqualityDmaintenanceA comprehensive MCP server providing 15 web tools including search, scraping, screenshots, SEO audits, and DNS/SSL checks through a single installation. It delivers clean, LLM-optimized outputs so AI agents can focus on reasoning rather than parsing raw HTML.156 npmMIT
- AlicenseAqualityDmaintenanceEnables AI agents to perform comprehensive SEO audits on web pages, including meta tags, headings, links, images, performance, and more, via a CLI or MCP server.181MIT