en es ja ko zh

mcp-jina-ai

Name: mcp-jina-ai
Author: JoeBuildsStuff

by JoeBuildsStuff

Overview Schema Source Related Servers Discussions Admin

JavaScript

Remote

Servidor Jina AI MCP

insignia de herrería

Un servidor MCP que proporciona acceso a los potentes servicios web de Jina AI a través de Claude. Este servidor implementa tres herramientas principales:

Lectura de páginas web y extracción de contenido
Búsqueda web
Verificación de hechos/fundamentación

Características

Herramientas

`read_webpage`

Extraer contenido de páginas web en un formato optimizado para LLM
Admite múltiples formatos de salida (predeterminado, Markdown, HTML, texto, captura de pantalla, captura de página)
Opciones para incluir enlaces e imágenes
Capacidad de generar texto alternativo para imágenes
Opciones de control de caché

`search_web`

Busque en la web utilizando la API de búsqueda de Jina AI
Número de resultados configurable (predeterminado: 5)
Soporte para retención de imágenes y generación de texto alternativo
Múltiples formatos de retorno (markdown, texto, html)
Devuelve resultados estructurados con títulos, descripciones y contenido.

`fact_check`

Verificar declaraciones utilizando el motor de puesta a tierra de Jina AI
Proporciona puntuaciones de factualidad y evidencia de respaldo.
Modo de inmersión profunda opcional para un análisis más exhaustivo
Devuelve referencias con citas clave y clasificación de apoyo/contradictoria

Related MCP server: sysauto Ask MCP Server

Configuración

Prerrequisitos

Necesitarás una clave API de Jina AI para usar este servidor. Consíguela gratis en https://jina.ai/

Instalación

Hay dos formas de utilizar este servidor:

Instalación mediante herrería

Para instalar Jina AI para Claude Desktop automáticamente a través de Smithery :

npx -y @smithery/cli install jina-ai-mcp-server --client claude

Opción 1: NPX (Recomendado)

Agregue esta configuración a su archivo de configuración de Claude Desktop:

{
  "mcpServers": {
    "jina-ai-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "jina-ai-mcp-server"
      ],
      "env": {
        "JINA_API_KEY": "<YOUR_KEY>"
      }
    }
  }
}

Opción 2: Instalación local

Clonar el repositorio
Instalar dependencias:

npm install

Construir el servidor:

npm run build

Agregue esta configuración a su configuración de Claude Desktop:

{
  "mcpServers": {
    "jina-ai-mcp-server": {
      "command": "node",
      "args": [
        "/path/to/jina-ai-mcp-server/dist/index.js"
      ],
      "env": {
        "JINA_API_KEY": "<YOUR_KEY>"
      }
    }
  }
}

Ubicación del archivo de configuración

En MacOS:

~/Library/Application Support/Claude/claude_desktop_config.json

En Windows:

%APPDATA%/Claude/claude_desktop_config.json

Depuración

Dado que los servidores MCP se comunican a través de stdio, la depuración puede ser complicada. Recomendamos usar el Inspector MCP :

npm run inspector

El Inspector proporcionará una URL para acceder a las herramientas de depuración en su navegador.

Tipos de respuesta de API

Todas las herramientas devuelven respuestas JSON estructuradas que incluyen:

Códigos de estado y metadatos
Contenido formateado según el tipo de salida solicitado
Información de uso (cantidad de tokens)
Cuando corresponda: imágenes, enlaces y metadatos adicionales

Para obtener información detallada del esquema, consulte schemas.ts .

Available Tools

3 tools

fact_checkC

Fact-check a statement using Jina AI's grounding engine

ParametersJSON Schema

Name	Required	Description	Default
`deepdive`	No
`statement`	Yes

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations, the description carries full burden but only states the action. It does not disclose whether the tool returns a verdict, an explanation, or requires additional context. No mention of side effects or limitations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely concise, single sentence, front-loaded with the main purpose. However, it sacrifices informative details that could be added without much length.

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?

Given no output schema, no annotations, and 2 parameters, the description lacks details on return values, error cases, and parameter behavior. It is insufficient for an agent to fully understand the tool's usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, and the description adds no meaning to parameters. The 'deepdive' boolean parameter is not explained. The description only mentions 'statement' implicitly.

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 verb 'fact-check', the resource 'a statement', and the specific engine 'Jina AI's grounding engine'. It distinguishes itself from sibling tools 'read_webpage' and 'search_web' by focusing on verification rather than retrieval.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. For instance, it does not clarify that fact-check should be used for verifying claims while search_web or read_webpage are for general information gathering.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

read_webpageC

Extract content from a webpage in a format optimized for LLMs

ParametersJSON Schema

Name	Required	Description	Default
`url`	Yes
`format`	No
`no_cache`	No
`with_links`	No
`with_images`	No
`with_generated_alt`	No

TDQS

C2.6/5.0

Behavior2/5

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

No annotations provided. Description lacks disclosure of caching, rate limits, error behavior, or format implications. 'Optimized for LLMs' is vague.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Very concise single sentence but at cost of completeness. Front-loads purpose but does not earn its place with meaningful detail.

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

Completeness1/5

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

Given 6 parameters, no output schema, and no annotations, the description is severely incomplete. Does not cover return values, parameter details, or behavioral aspects.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%. Description does not explain any of the 6 parameters (e.g., format, no_cache, with_links). Fails to compensate for missing schema descriptions.

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?

Description clearly states verb 'extract', resource 'content from a webpage', and purpose 'optimized for LLMs'. It distinguishes from siblings like 'fact_check' and 'search_web'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use vs siblings or when not to use. Lacks context for selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_webC

Search the web using Jina AI's search API

ParametersJSON Schema

Name	Required	Default
`count`	No
`query`	Yes
`retain_images`	No	none
`return_format`	No	markdown
`with_generated_alt`	No

TDQS

C2.4/5.0

Behavior2/5

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

With no annotations, the description must carry the full burden. It only states 'search the web' without disclosing behavioral traits like rate limits, result count limits, or idempotency. The minimal info does not cover core behavioral expectations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence, which is concise but overly minimal. It lacks structure and does not effectively organize details; brevity here sacrifices completeness.

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?

Given 5 parameters, no output schema, no annotations, and sibling tools, the description is incomplete. It fails to explain return format, parameter effects, or differentiate from related tools, leaving significant gaps for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 5 parameters with 0% description coverage. The description adds no meaning beyond the schema fields. Parameters like 'count', 'retain_images', and 'return_format' are left unexplained, forcing the agent to guess their semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches the web using Jina AI's API, identifying the verb and resource. However, it does not differentiate from sibling tools like fact_check or read_webpage, missing an opportunity to clarify scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. The description lacks explicit context or exclusions, leaving the agent to infer usage independently.

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.

3 tool updatesv1.0.1Jul 4, 2026
- First observedfact_check
- First observedread_webpage
- First observedsearch_web

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

B3.3/5.0

Scored 2026-07-04 12:40 across 3 tools

Disambiguation5/5

Each tool targets a distinct operation: fact-checking a statement, reading a webpage, and searching the web. No overlap in purpose.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern using snake_case: fact_check, read_webpage, search_web.

Tool Count5/5

With three tools, the set is well-scoped for a web and grounding server, covering the core tasks without extraneous tools.

Completeness5/5

The tool surface covers the essential workflow: search, retrieve, and verify. No obvious gaps given the server's apparent purpose.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

Need Help?

Related Servers

Related MCP Connectors

Remote Desktop CommanderOAuth
app.desktopcommander
Hosted MCP server connecting claude.ai, ChatGPT and other AI apps to your own computer
Shell Access OS Automation
2105
Danushkumar-V-mcp-discordOAuth
ai.smithery
An MCP server that integrates with Discord to provide AI-powered features.
Communication Social Media App Automation
2
mcp-openaiOAuth
io.github.AceDataCloud
MCP server for OpenAI API (chat completions, image generation, embeddings) via AceDataCloud
AI & Machine Learning Image & Video Processing
11
mcp-aichatOAuth
io.github.AceDataCloud
MCP server for AI dialogue using various LLM models via AceDataCloud
AI & Machine Learning
2

Related MCP Servers

Perplexity MCP Server
Search RAG Systems
RossH121
A
license
A
quality
D
maintenance
An MCP server that enables Claude to perform web searches using Perplexity's API with intelligent model selection based on query intent and support for domain and recency filtering.
Updated 4 months ago (2026-05-31 22:10 UTC)
6
4
MIT
sysauto Ask MCP Server
Browser Automation Search Web Scraping
rogeriolembo
A
license
Not graded
quality
D
maintenance
An MCP server that integrates with Sonar API to provide Claude with real-time web search capabilities for comprehensive research.
Updated 4 months ago (2026-05-22 03:16 UTC)
7 npm
MIT
You.com MCP Server
Search Browser Automation Research & Data
jimbul
A
license
D
quality
F
maintenance
MCP server that provides Claude AI assistants with the ability to search the web, get news, and perform research using the You.com API.
Updated a year ago (2025-05-05 15:55 UTC)
4
2
MIT
openai-mcp
AI & Machine Learning Developer Tools
robotlearning123
A
license
Not graded
quality
B
maintenance
MCP server that bridges ChatGPT Plus/Pro to Claude Code, enabling chat, deep research, and image generation via your own account.
Updated 2 days ago (2026-10-04 15:00 UTC)
172 PyPI
50
MIT

Servidor Jina AI MCP

Características

Herramientas

read_webpage

search_web

fact_check

Configuración

Prerrequisitos

Instalación

Instalación mediante herrería

Opción 1: NPX (Recomendado)

Opción 2: Instalación local

Ubicación del archivo de configuración

Depuración

Tipos de respuesta de API

Available Tools

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Appeared in Searches

`read_webpage`

`search_web`

`fact_check`