ai-visibility-mcp
ai-visibility-mcp
Un servidor MCP que le indica a un asistente de IA lo visible que es un sitio web para los motores de búsqueda y asistentes de IA (ChatGPT, Claude, Perplexity, Google AI Overviews).
Pásale una URL e informa de las señales que determinan si esos sistemas pueden encontrar, leer y citar un sitio, y luego las condensa en una puntuación de 0 a 100 fácil de leer de un vistazo.
Qué comprueba
Señal | Qué y por qué importa |
Acceso de los rastreadores de IA en | Si |
Presencia de | Un mapa en texto plano de los datos clave del sitio, escrito para LLMs |
Datos estructurados (JSON-LD) | Significado legible por máquinas que los motores de respuesta extraen y citan |
Presencia de | Ayuda a los rastreadores a encontrar todas las páginas |
Título, meta descripción y H1 | Señales básicas en la página que un motor lee primero |
Related MCP server: maxaeo-ai-visibility-mcp
Instalación
git clone https://github.com/draltaway/ai-visibility-mcp
cd ai-visibility-mcp
python -m pip install -e .Solo HTTP de la biblioteca estándar. La única dependencia es FastMCP.
Cómo usarlo desde Claude Code
claude mcp add ai-visibility -- ai-visibility-mcpCómo usarlo desde Claude Desktop
Añade a claude_desktop_config.json:
{
"mcpServers": {
"ai-visibility": {
"command": "ai-visibility-mcp"
}
}
}Luego pregunta: "Comprueba la visibilidad de IA de stripe.com."
Ejemplo de salida
{
"url": "https://stripe.com",
"score": 90,
"summary": "Strong: AI engines can find, read, and cite this site.",
"checks": {
"ai_crawlers_allowed": true,
"ai_crawlers_blocked": [],
"llms_txt": true,
"sitemap": false,
"homepage_reachable": true,
"structured_data": true,
"title": true,
"meta_description": true,
"h1": true
}
}La herramienta
check_ai_visibility(url: str) -> dict devuelve { url, score, summary, checks }. Acepta un host simple (example.com) o una URL completa y por defecto usa https.
Licencia
MIT. Creado por Dallin Rowley.
Available Tools
1 toolcheck_ai_visibilityA
Check how discoverable a website is to AI assistants and AI search engines.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The site to check, e.g. "example.com" or "https://example.com". |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure, and it only states the tool's purpose. It does not reveal whether the check involves a live network fetch, what signals are analyzed (e.g., robots.txt, llms.txt, meta tags), or whether results are instantaneous or sampled. Nothing is disclosed that contradicts annotations because no annotations exist.
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 a single, front-loaded sentence with zero wasted words, stating the action before the object. It is efficient, though slightly terse — a phrase clarifying what 'discoverable' means could be added without hurting conciseness.
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?
This is a low-complexity tool with one required parameter and an output schema present, so the description does not need to describe return values and the input is fully documented in the schema. The main context gap is the absence of any explanation of what 'AI discoverability' is based on, but for such a simple tool the definition is nearly complete.
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% — the url parameter already documents its type and accepted formats ('example.com' or 'https://example.com'). The tool description adds no additional meaning about the parameter, so the baseline 3 is appropriate.
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 states a clear action ('Check') and a specific resource/scope ('how discoverable a website is to AI assistants and AI search engines'), so an agent can tell what the tool does at a glance. There are no sibling tools to distinguish from, and the term 'discoverability' is left slightly undefined, so it falls just short of a 5.
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?
Because there are no sibling tools, there are no alternatives to exclude, but the description still provides only implied use context: if an agent wants to know a site's AI visibility, this is the tool. There is no explicit when-to-use guidance, exclusions, or prerequisites, making the usage guidance minimally adequate.
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
v0.1.0- First observed
check_ai_visibility
TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion or overlap. The tool's purpose is clearly unique and distinct.
A single tool with a descriptive snake_case verb_noun name. There is no inconsistency to assess, but the naming is clear and follows conventional MCP patterns.
A single tool feels thin for a dedicated MCP server, even for a focused purpose. The borderline count leaves little room for related operations but is not excessively sparse.
The tool directly covers the core stated purpose of checking AI visibility. Minor gaps exist, such as batch checks or historical analysis, but they do not block the primary use case.
Related MCP Connectors
Score any website's AI search visibility out of 100 and see which of 20 AI crawlers can read it.
Audit any site's AI visibility from your assistant: crawler access, rendering, and schema.
Checks llms.txt, AI crawler access in robots.txt, and sitemap - with a 0-100 AI readiness score.
AEO audit: score any website 0-100 for AI visibility. Checks schema, meta, content, AI crawlers.
Related MCP Servers
- AlicenseNot gradedqualityCmaintenanceEvaluates any website's AI visibility with 15 checks across crawlability, structure, content, and connectivity, and provides actionable fixes.4 npmMIT
- AlicenseAqualityBmaintenanceEnables AI agents to check whether a public website is crawlable, understandable, and ready for AI search workflows through local-only audits of robots.txt, sitemaps, metadata, and llms.txt.371 npm1MIT
- AlicenseAqualityCmaintenanceCheck whether a website is visible to AI search engines (ChatGPT, Perplexity, Claude, Google AI Overviews). Returns a 0-100 readiness score, a grade, and a specific fix for each gap. Dependency-free, no API keys.23 npmMIT
- AlicenseAqualityBmaintenanceProvides AI-visibility scoring and site auditing capabilities for websites, enabling agents to check how sites appear in AI engines like ChatGPT and Perplexity, run full SEO/security audits, and monitor changes over time.20333 npm1MIT