G-Search MCP
G-Search MCP
Un potente servidor MCP para la búsqueda de Google que permite realizar búsquedas paralelas con múltiples palabras clave simultáneamente.
Este proyecto es una modificación de la búsqueda de Google .
Ventajas
Búsqueda paralela : admite la búsqueda con múltiples palabras clave en Google simultáneamente, lo que mejora la eficiencia de la búsqueda.
Optimización del navegador : abre varias pestañas en una sola instancia del navegador para una búsqueda paralela eficiente
Manejo automático de verificación : detecta de forma inteligente CAPTCHA y habilita el modo de navegador visible para la verificación del usuario cuando sea necesario
Simulación del comportamiento del usuario : simula patrones de navegación de usuarios reales para reducir la posibilidad de detección por parte de los motores de búsqueda.
Datos estructurados : devuelve resultados de búsqueda estructurados en formato JSON para facilitar su procesamiento y análisis.
Parámetros configurables : admite varias configuraciones de parámetros, como límites de resultados de búsqueda, configuraciones de tiempo de espera, configuraciones regionales, etc.
Related MCP server: Enhanced Google Search MCP
Inicio rápido
Ejecutar directamente con npx:
npx -y g-search-mcpPrimera configuración: instale el navegador requerido ejecutando el siguiente comando en su terminal:
npx playwright install chromiumModo de depuración
Utilice la opción --debug para ejecutar en modo de depuración (mostrando la ventana del navegador):
npx -y g-search-mcp --debugConfigurar MCP
Configure este servidor MCP en Claude Desktop:
MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"g-search": {
"command": "npx",
"args": ["-y", "g-search-mcp"]
}
}
}Características
search- Ejecutar búsquedas de Google con múltiples palabras clave y devolver resultadosUtiliza el navegador Playwright para realizar búsquedas
Admite los siguientes parámetros:
queries: Matriz de consultas de búsqueda a ejecutar (parámetro obligatorio)limit: Número máximo de resultados a devolver por consulta, el valor predeterminado es 10timeout: Tiempo de espera de carga de la página en milisegundos, el valor predeterminado es 60000 (60 segundos)noSaveState: si se debe evitar guardar el estado del navegador, el valor predeterminado es falsolocale: configuración regional para los resultados de búsqueda, el valor predeterminado es en-USdebug: si se habilita el modo de depuración (mostrando la ventana del navegador), anula el indicador --debug en la línea de comando
Ejemplo de uso :
Use the search tool to search for "machine learning" and "artificial intelligence" on GoogleEjemplo de respuesta :
{
"searches": [
{
"query": "machine learning",
"results": [
{
"title": "What is Machine Learning? | IBM",
"link": "https://www.ibm.com/topics/machine-learning",
"snippet": "Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy."
},
...
]
},
{
"query": "artificial intelligence",
"results": [
{
"title": "What is Artificial Intelligence (AI)? | IBM",
"link": "https://www.ibm.com/topics/artificial-intelligence",
"snippet": "Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind."
},
...
]
}
]
}Consejos de uso
Manejo de escenarios especiales de sitios web
Ajuste de los parámetros de búsqueda
Cantidad de resultados de búsqueda : Para obtener más resultados de búsqueda:
Please return the top 20 search results for each keywordEsto establecerá el
limit: 20parámetros.Aumentar la duración del tiempo de espera : para situaciones de carga lenta:
Please set the page loading timeout to 120 secondsEsto ajustará el parámetro de
timeouta 120000 milisegundos.
Ajuste de la configuración regional
Cambiar región de búsqueda : especifique una configuración regional diferente:
Please use Chinese locale (zh-CN) for searchingEsto establecerá el parámetro
locale: "zh-CN".
Depuración y solución de problemas
Habilitar el modo de depuración
Activación de depuración dinámica : para mostrar la ventana del navegador durante una operación de búsqueda específica:
Please enable debug mode for this search operationEsto establece
debug: trueincluso si el servidor se inició sin el indicador--debug.
Instalación
Prerrequisitos
Node.js 18 o superior
NPM o Hilo
Instalar desde la fuente
Clonar el repositorio:
git clone https://github.com/jae-jae/g-search-mcp.git
cd g-search-mcpInstalar dependencias:
npm installInstalar el navegador Playwright:
npm run install-browserConstruir el servidor:
npm run buildDesarrollo
Reconstrucción automática (modo de desarrollo)
npm run watchUso de MCP Inspector para la depuración
npm run inspectorProyectos relacionados
fetcher-mcp : Un potente servidor MCP para obtener contenido de páginas web mediante el navegador sin interfaz gráfica Playwright. Ofrece extracción inteligente de contenido, procesamiento paralelo, optimización de recursos y más, lo que lo convierte en una herramienta ideal para el scraping de contenido web.
Licencia
Con licencia MIT
Available Tools
1 toolsearchC
Search on Google for multiple keywords and return the results
| Name | Required | Description | Default |
|---|---|---|---|
| debug | No | Whether to enable debug mode (showing browser window), overrides the --debug command line flag if specified | |
| limit | No | Maximum number of results to return per query (default: 10) | |
| locale | No | Locale setting for search results (default: en-US) | |
| noSaveState | No | Whether to avoid saving browser state (default: false) | |
| queries | Yes | Array of search queries to perform | |
| timeout | No | Page loading timeout in milliseconds (default: 60000) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the action (search Google) and output (return results) but omits critical behavioral details like rate limits, authentication requirements, error handling, or what format results are returned in. This leaves significant gaps for an AI agent to understand tool behavior.
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 extremely concise - a single sentence that efficiently communicates the core functionality. Every word earns its place with no redundant information. The structure is front-loaded with the essential action and purpose.
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?
For a tool with 6 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what format results are returned in, how errors are handled, or provide context about the search operation's limitations. The agent would need to guess about important behavioral aspects.
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%, providing comprehensive parameter documentation. The description adds minimal value beyond the schema, only implying that 'queries' parameter contains search keywords. No additional semantic context is provided about parameter interactions or usage patterns.
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: 'Search on Google for multiple keywords and return the results.' It specifies the verb (search), resource (Google), and scope (multiple keywords). However, with no sibling tools mentioned, there's no opportunity to differentiate from alternatives, preventing a perfect score.
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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, constraints, or typical use cases. While there are no sibling tools to contrast with, the description lacks any contextual usage information.
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
search
TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion or overlap between tools, making disambiguation perfect. The single tool has a clear and distinct purpose without any ambiguity.
With only one tool, naming consistency is inherently perfect as there are no other tools to compare against. The tool name 'search' follows a simple verb pattern, which is appropriate for its function.
A single tool is too few for a search server, as it lacks basic operations like refining searches, handling different result types, or managing search history. This minimal set feels thin and incomplete for the apparent scope of a search service.
The tool set is severely incomplete for a search domain, offering only a basic search function without any support for advanced features like filtering, pagination, or accessing specific result details. This will likely cause agent failures when more complex search tasks are required.
Maintenance
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