Jina.ai Grounding MCP Server
mcp-jinaai-puesta a tierra
⚠️ 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 Base de Jina.ai con LLM. Este servidor proporciona capacidades de base de contenido web eficientes y completas, optimizadas para mejorar las respuestas de LLM con contenido web factual en tiempo real.
Related MCP server: MCP JinaAI Search Server
Características
🌐 Puesta a tierra avanzada de contenido web mediante la API de puesta a tierra de Jina.ai
🚀 Verificación de contenido y verificación de hechos en tiempo real
📚 Análisis integral de contenido web
Formato limpio optimizado para LLM
🎯 Puntuación precisa de relevancia del contenido
🏗️ 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-grounding": {
"command": "node",
"args": ["-y", "mcp-jinaai-grounding"],
"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-grounding": {
"command": "wsl.exe",
"args": [
"bash",
"-c",
"JINAAI_API_KEY=your-jinaai-api-key npx mcp-jinaai-grounding"
]
}
}
}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 herramientas MCP para fundamentar las respuestas LLM con contenido web:
contenido_del_suelo
Respuestas LLM terrestres con contenido web en tiempo real utilizando Jina.ai Grounding.
Parámetros:
query(cadena, obligatoria): El texto que se va a utilizar como base para el contenido webno_cache(booleano, opcional): Omite la caché para resultados actualizados. El valor predeterminado es falso.format(cadena, opcional): Formato de respuesta ("json" o "texto"). El valor predeterminado es "texto".token_budget(número, opcional): Número máximo de tokens para esta solicitudbrowser_locale(cadena, opcional): configuración regional del navegador para representar el contenidostream(booleano, opcional): Habilita el modo de transmisión para páginas grandes. El valor predeterminado es falso.gather_links(booleano, opcional): Recopila todos los enlaces al final de la respuesta. El valor predeterminado es falso.gather_images(booleano, opcional): Recopila todas las imágenes al final de la respuesta. El valor predeterminado es falso.image_caption(booleano, opcional): Subtitula las imágenes del contenido. El valor predeterminado es falso.enable_iframe(booleano, opcional): Extrae contenido de los iframes. El valor predeterminado es falso.enable_shadow_dom(booleano, opcional): Extrae contenido del shadow DOM. El valor predeterminado es falso.resolve_redirects(booleano, opcional): Sigue las cadenas de redireccionamiento hasta la URL final. Valor predeterminado: verdadero.
Desarrollo
Configuración
Clonar el repositorio
Instalar dependencias:
pnpm installConstruir el proyecto:
pnpm run buildEjecutar en modo de desarrollo:
pnpm run devPublicación
Actualizar la versión en package.json
Construir el proyecto:
pnpm run buildPublicar en npm:
pnpm run releaseContribuyendo
¡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
Construido sobre el Protocolo de Contexto Modelo
Desarrollado por la API de conexión a tierra de Jina.ai
Available Tools
1 toolground_statementA
Ground a statement using real-time web search results to check factuality. When providing URLs via the references parameter, ensure they are publicly accessible and contain relevant information about the statement. If the URLs do not contain the necessary information, try removing the URL restrictions to search the entire web.
| Name | Required | Description | Default |
|---|---|---|---|
| statement | Yes | Statement to be grounded | |
| references | No | Optional list of URLs to restrict search to. Only provide URLs that are publicly accessible and contain information relevant to the statement. If the URLs do not contain the necessary information, the grounding will fail. For best results, either provide URLs you are certain contain the information, or omit this parameter to search the entire web. | |
| no_cache | No | Whether to bypass cache for fresh results |
TDQS
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 describes key traits like using real-time web search, the impact of URL restrictions (grounding may fail if URLs lack info), and the option to bypass cache. However, it omits details such as rate limits, authentication needs, or specific error handling, leaving some behavioral aspects unclear.
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 appropriately sized and front-loaded, starting with the core purpose. Both sentences earn their place by adding useful context about URL handling, though it could be slightly more streamlined by avoiding minor redundancy with the schema (e.g., repeating URL accessibility advice).
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?
Given the tool's complexity (fact-checking with web search) and no annotations or output schema, the description is moderately complete. It covers the main purpose and parameter usage but lacks details on output format, error cases, or performance expectations, which are important for an agent to use it effectively without structured output guidance.
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 all parameters thoroughly. The description adds minimal value beyond the schema by reiterating guidance on the references parameter (e.g., ensuring URLs are accessible and relevant), but it doesn't provide additional semantic context or examples not covered in the schema, warranting a baseline score.
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 clearly states the tool's purpose with a specific verb ('ground') and resource ('statement'), explaining it uses real-time web search to check factuality. It distinguishes the action from generic search by specifying the grounding objective, and with no sibling tools, this level of specificity is excellent.
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 provides clear context on when to use the tool (for fact-checking statements) and includes guidance on the references parameter (e.g., ensure URLs are publicly accessible and relevant, or omit to search the entire web). However, it lacks explicit alternatives or exclusions, as there are no sibling tools, so it doesn't fully address when-not-to-use 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 tool update
v1.0.0- First observed
ground_statement
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'ground_statement' has a clearly defined and distinct purpose: fact-checking statements using web search.
Since there is only one tool, naming consistency is inherently perfect. The tool name 'ground_statement' follows a clear verb_noun pattern, which would be consistent if more tools were added.
A single tool is too few for a server that appears to handle grounding/verification tasks, as it suggests an incomplete or minimal surface. Typically, such a domain might include tools for different grounding methods, batch processing, or related operations.
The server is severely incomplete for its apparent grounding/fact-checking domain. It lacks essential operations like grounding multiple statements, verifying against specific sources, or handling different input formats, which limits agent workflows.
Maintenance
Related MCP Connectors
Real-time fact-check, citation verification, and source-freshness for AI agents.
Jina AI Reader/Search MCP — turn any URL into clean LLM-ready markdown, plus web search.
Enable language models to perform advanced AI-powered web scraping with enterprise-grade reliabili…
Verified, sourced, real-time intelligence layer for AI agents.
Related MCP Servers
- AlicenseBqualityFmaintenanceIntegrates Jina.ai's Reader API with LLMs for efficient and structured web content extraction, optimized for documentation and web content analysis.120 npm29MIT
- AlicenseBqualityFmaintenanceEnables efficient web search integration with Jina.ai's Search API, offering clean, LLM-optimized content retrieval with support for various content types and configurable caching.118 npm3MIT
- AlicenseNot gradedqualityDmaintenanceEnables web content retrieval and semantic search capabilities through the Jina AI API. Provides tools to fetch content from URLs and perform intelligent web searches with natural language queries.3MIT
- AlicenseNot gradedqualityCmaintenanceProvides access to Jina AI's web reading, search, embeddings, and reranking capabilities. Enables URL content extraction, web/arXiv/image search, document deduplication, and relevance ranking through natural language.Apache 2.0