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MCP JinaAI Reader Server

by spences10
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

Lector mcp-jinaai

⚠️ Aviso

Este repositorio ya no se mantiene.

La funcionalidad de esta herramienta ahora está disponible en mcp-omnisearch , que combina múltiples herramientas MCP en un paquete unificado.

Utilice mcp-omnisearch en su lugar.

Un servidor de Protocolo de Contexto de Modelo (MCP) para integrar la API de Lector de Jina.ai con LLM. Este servidor proporciona capacidades de extracción de contenido web eficientes y completas, optimizadas para la documentación y el análisis de contenido web.

Related MCP server: Jina AI Remote MCP Server

Características

  • Extracción avanzada de contenido web a través de la API del lector Jina.ai

  • 🚀 Recuperación de contenido rápida y eficiente

  • 📄 Extracción de texto completa con estructura preservada

  • Formato limpio optimizado para LLM

  • 🌐 Soporte para varios tipos de contenido, incluida documentación

  • 🏗️ Construido sobre el Protocolo de Contexto Modelo

Configuración

Este servidor requiere configuración a través de su cliente MCP. A continuación, se muestran ejemplos para diferentes entornos:

Configuración de Cline

Agregue esto a su configuración de Cline MCP:

{
	"mcpServers": {
		"jinaai-reader": {
			"command": "node",
			"args": ["-y", "mcp-jinaai-reader"],
			"env": {
				"JINAAI_API_KEY": "your-jinaai-api-key"
			}
		}
	}
}

Escritorio Claude con configuración WSL

Para entornos WSL, agregue esto a su configuración de Claude Desktop:

{
	"mcpServers": {
		"jinaai-reader": {
			"command": "wsl.exe",
			"args": [
				"bash",
				"-c",
				"JINAAI_API_KEY=your-jinaai-api-key npx mcp-jinaai-reader"
			]
		}
	}
}

Variables de entorno

El servidor requiere la siguiente variable de entorno:

  • JINAAI_API_KEY : Su clave API de Jina.ai (obligatoria)

API

El servidor implementa una única herramienta MCP con parámetros configurables:

leer_url

Convierta cualquier URL en texto compatible con LLM utilizando Jina.ai Reader.

Parámetros:

  • url (cadena, obligatoria): URL a procesar

  • no_cache (booleano, opcional): Omite la caché para resultados actualizados. El valor predeterminado es falso.

  • format (cadena, opcional): Formato de respuesta ("json" o "stream"). El valor predeterminado es "json".

  • timeout (número, opcional): tiempo máximo en segundos para esperar la carga de la página web

  • target_selector (cadena, opcional): selector CSS para centrarse en elementos específicos

  • wait_for_selector (cadena, opcional): selector CSS para esperar elementos específicos

  • remove_selector (cadena, opcional): selector CSS para excluir elementos específicos

  • with_links_summary (booleano, opcional): Recopilar todos los enlaces al final de la respuesta

  • with_images_summary (booleano, opcional): Recopilar todas las imágenes al final de la respuesta

  • with_generated_alt (booleano, opcional): agrega texto alternativo a las imágenes que no tienen subtítulos

  • with_iframe (booleano, opcional): incluye contenido iframe en la respuesta

Desarrollo

Configuración

  1. Clonar el repositorio

  2. Instalar dependencias:

npm install

  1. Construir el proyecto:

npm run build

  1. Ejecutar en modo de desarrollo:

npm run dev

Publicación

  1. Actualizar la versión en package.json

  2. Construir el proyecto:

npm run build

  1. Publicar en npm:

npm publish

Contribuyendo

¡Agradecemos sus contribuciones! No dude en enviar una solicitud de incorporación de cambios.

Licencia

Licencia MIT: consulte el archivo LICENCIA para obtener más detalles.

Expresiones de gratitud

Available Tools

1 tool
read_urlB

Convert any URL to LLM-friendly text using Jina.ai Reader

ParametersJSON Schema
NameRequiredDescriptionDefault
urlYesURL to process
no_cacheNoBypass cache for fresh results
formatNoResponse format (json or stream)json
timeoutNoMaximum time in seconds to wait for webpage load
target_selectorNoCSS selector to focus on specific elements
wait_for_selectorNoCSS selector to wait for specific elements
remove_selectorNoCSS selector to exclude specific elements
with_links_summaryNoGather all links at the end of response
with_images_summaryNoGather all images at the end of response
with_generated_altNoAdd alt text to images lacking captions
with_iframeNoInclude iframe content in response

TDQS

B3.4/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but only states the basic function without disclosing behavioral traits like rate limits, authentication needs, error handling, or performance characteristics. It mentions the external service (Jina.ai Reader) but doesn't explain implications of using a third-party service.

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 a single, efficient sentence that clearly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded with the core functionality, making every word earn its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex tool with 11 parameters and no output schema, the description is insufficient. It doesn't explain what 'LLM-friendly text' means in practice, doesn't describe the response format, and provides no guidance on parameter interactions or error cases. The lack of output schema increases the need for more descriptive context.

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?

Schema description coverage is 100%, providing comprehensive parameter documentation. The description adds no parameter-specific information beyond the schema, maintaining the baseline score. It doesn't explain relationships between parameters or provide usage examples.

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 verb ('Convert') and resource ('any URL') while specifying the method ('using Jina.ai Reader') and output format ('LLM-friendly text'). It distinguishes this as a URL-to-text conversion tool with no siblings to differentiate from.

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 context ('Convert any URL to LLM-friendly text') but provides no explicit guidance on when to use this tool versus alternatives, prerequisites, or limitations. With no sibling tools, the baseline is adequate but lacks specific usage scenarios.

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. 1 tool updatev1.0.0
    • First observedread_url
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.4/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool's purpose is clearly defined and distinct by default.

Naming Consistency5/5

A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The tool name 'read_url' follows a clear verb_noun pattern.

Tool Count2/5

One tool is too few for a server with a purpose that could reasonably support more operations, such as handling different URL types or providing metadata extraction. This minimal set feels thin and under-scoped for the domain.

Completeness2/5

The server's domain appears to be URL content reading, but the single tool only covers basic text conversion. There are obvious gaps, such as no tools for handling errors, extracting structured data, or managing different content formats, which limits agent effectiveness.

Maintenance

ActivityInactive
ResponsivenessNo issues

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