F
licenseNot graded
qualityD
maintenanceFetches web pages and converts them to clean, readable markdown format by extracting main content while removing navigation, ads, and other non-essential elements to minimize token usage.
4
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Mozilla Readability Parser MCP Server
by emzimmer
Servidor MCP del analizador de legibilidad de Mozilla
Un servidor de protocolo de contexto de modelo (MCP) que extrae y transforma el contenido de páginas web en Markdown limpio y optimizado para LLM. Devuelve el título del artículo, el contenido principal, un extracto, la firma y el nombre del sitio. Utiliza el algoritmo de legibilidad de Mozilla para eliminar anuncios, navegación, pies de página y elementos no esenciales, a la vez que conserva la estructura principal del contenido. Más información sobre MCP .
Características
Elimina anuncios, navegación, pies de página y otro contenido no esencial.
Convierte HTML limpio en Markdown bien formateado (también utiliza Turndown)
Devuelve metadatos del artículo (título, extracto, firma, nombre del sitio)
Maneja los errores con elegancia
Related MCP server: cleanfetch
¿Por qué no simplemente buscar?
A diferencia de las solicitudes de búsqueda simples, este servidor:
Extrae únicamente contenido relevante utilizando el algoritmo de legibilidad de Mozilla
Elimina ruidos como anuncios, ventanas emergentes y menús de navegación.
Reduce el uso de tokens al eliminar HTML/CSS innecesarios
Proporciona un formato Markdown consistente para un mejor procesamiento de LLM
Incluye metadatos útiles sobre el contenido.
Instalación
Instalación mediante herrería
Para instalar Mozilla Readability Parser para Claude Desktop automáticamente a través de Smithery :
npx -y @smithery/cli install server-moz-readability --client claudeInstalación manual
npm install server-moz-readabilityReferencia de herramientas
parse
Obtiene y transforma el contenido de la página web en Markdown limpio.
Argumentos:
{
"url": {
"type": "string",
"description": "The website URL to parse",
"required": true
}
}Devoluciones:
{
"title": "Article title",
"content": "Markdown content...",
"metadata": {
"excerpt": "Brief summary",
"byline": "Author information",
"siteName": "Source website name"
}
}Uso con Claude Desktop
Añade a tu claude_desktop_config.json :
{
"mcpServers": {
"readability": {
"command": "npx",
"args": ["-y", "server-moz-readability"]
}
}
}Dependencias
@mozilla/readability - Extracción de contenido
Turndown - Conversión de HTML a Markdown
jsdom - Análisis del DOM
axios - Solicitudes HTTP
Licencia
Instituto Tecnológico de Massachusetts (MIT)
Available Tools
1 toolparseA
Extracts and transforms webpage content into clean, LLM-optimized Markdown. Returns article title, main content, excerpt, byline and site name. Uses Mozilla's Readability algorithm to remove ads, navigation, footers and non-essential elements while preserving the core content structure.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The website URL to parse |
TDQS
A4/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: the transformation process ('extracts and transforms'), the algorithm used ('Mozilla's Readability algorithm'), what gets removed ('ads, navigation, footers and non-essential elements'), and what is preserved ('core content structure'). However, it doesn't mention potential limitations like rate limits, authentication needs, or error conditions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with two sentences that efficiently convey the tool's purpose, output, and key behavioral traits. Every sentence adds value without redundancy, making it easy to understand at a glance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (single parameter, no output schema, no annotations), the description is largely complete. It explains what the tool does, how it processes content, and what it returns. However, without an output schema, it could benefit from more detail on the return structure (e.g., format of the Markdown), and it lacks information on error handling or edge cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the parameter 'url' clearly documented as 'The website URL to parse'. The description doesn't add any additional meaning or context about the parameter beyond what the schema provides, such as URL format requirements or examples. With high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('extracts and transforms') and resources ('webpage content'), specifying the output format ('clean, LLM-optimized Markdown') and what it returns ('article title, main content, excerpt, byline and site name'). It distinguishes itself by mentioning the algorithm used ('Mozilla's Readability algorithm') and what it removes ('ads, navigation, footers and non-essential elements').
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for extracting structured content from webpages, but does not explicitly state when to use this tool versus alternatives, nor provide exclusions or prerequisites. With no sibling tools, the lack of explicit guidelines is less critical, but it still doesn't offer clear when/when-not instructions.
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
parse
TDQS
A3.9/5.0
Scored across 1 tool
Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clear and distinct purpose focused on parsing webpage content into clean Markdown.
Naming Consistency5/5
A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The tool name 'parse' is straightforward and appropriate for its function.
Tool Count2/5
A single tool is too few for a server's purpose, even if that purpose is narrow. This limits functionality and makes the server feel thin, as it lacks complementary operations like configuration, validation, or batch processing that might be expected in a parsing domain.
Completeness2/5
The tool surface is severely incomplete for a parsing server. While the 'parse' tool covers the core extraction function, there are obvious gaps such as no tools for handling errors, validating inputs, managing configurations, or providing metadata about the parsing process, which could lead to agent failures in real-world scenarios.
Maintenance
ActivityInactive
ResponsivenessUnresponsive
Related MCP Connectors
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Read any public web page as clean Markdown for LLMs, with its metadata. Paid per call, x402.
Related MCP Servers
- AlicenseAqualityCmaintenanceEnables AI agents to read web pages reliably, returning clean markdown content, hyperlinks, and metadata without navigation or ad noise.39 npmMIT
- FlicenseNot gradedqualityDmaintenanceFetches webpages and returns clean, structured Markdown with metadata (title, author, publish date, description, domain, word count).1-
- AlicenseNot gradedqualityDmaintenanceConverts any webpage into clean, LLM-ready Markdown, removing noise and supporting JavaScript rendering.MIT