A
licenseB
qualityD
maintenanceProvides web search capabilities using Google Custom Search API, enabling users to perform searches through a Model Context Protocol server.
2
255 npm
69
MIT
MCP Server for Google Search
by mcp-for-dev
Servidor MCP para la Búsqueda de Google
Un servidor de protocolo de contexto de modelo que proporciona capacidades de búsqueda web mediante la API de búsqueda personalizada de Google y la funcionalidad de extracción de contenido de páginas web.
Herramientas
Buscar
Realice búsquedas web utilizando la API de búsqueda personalizada de Google:
Busque en toda la web o en sitios específicos
Controlar el número de resultados (1-10)
Obtenga resultados estructurados con título, enlace y fragmento
Lector de páginas web
Extraer contenido de cualquier página web:
Obtener y analizar el contenido de la página web
Extraer el título de la página y el texto principal
Limpiar el contenido eliminando scripts y estilos
Devuelve datos estructurados con título, texto y URL
Related MCP server: MCP Google Custom Search Server
Instalación
Obtener la clave API de Google y el ID del motor de búsqueda
Crear un proyecto de Google Cloud:
Crea un nuevo proyecto o selecciona uno existente
Habilitar la facturación para su proyecto
Habilitar API de búsqueda personalizada:
Ir a la biblioteca API
Buscar "API de búsqueda personalizada"
Haga clic en "Habilitar"
Obtener clave API:
Ir a Credenciales
Haga clic en "Crear credenciales" > "Clave API".
Copia tu clave API
(Opcional) Restrinja la clave API solo a la API de búsqueda personalizada
Crear un motor de búsqueda personalizado:
Ingrese los sitios que desea buscar (use www.google.com para búsquedas web generales)
Haga clic en "Crear"
En la página siguiente, haga clic en "Personalizar".
En la configuración, activa "Buscar en toda la web".
Copia tu ID de motor de búsqueda (cx)
Configuración del cliente
Para usar con Claude Desktop, agregue la configuración del servidor con sus credenciales de API de Google:
En MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json En Windows: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"google-search": {
"command": "npx",
"args": ["-y", "@mcp-for-dev/mcp-google-search"],
"env": {
"GOOGLE_API_KEY": "your-api-key-here",
"GOOGLE_SEARCH_ENGINE_ID": "your-search-engine-id-here"
}
}
}
}Available Tools
2 toolsgoogle_searchC
Perform a web search query
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query | |
| num | No | Number of results (1-10) |
TDQS
C2.8/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description does not disclose any behavioral traits such as rate limits, caching, or return format. The agent is left without important context for safe invocation.
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 concise sentence, but it is too minimal. While there is no wasted text, it lacks structure (e.g., separating purpose from usage details).
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 the simple structure (2 parameters, no output schema), the description fails to mention return behavior or result format, leaving the agent unaware of what to expect after invocation.
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 input schema has 100% description coverage for both parameters, so the schema itself provides the meaning. The description adds no further semantic value beyond restating the schema.
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 'Perform a web search query' clearly indicates the tool's verb and resource, distinguishing it from the sibling 'read_webpage' which reads a specific page.
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 vs. alternatives (e.g., read_webpage) or any prerequisites. The description lacks explicit usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
read_webpageA
Fetch and extract text content from a webpage
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL of the webpage to read |
TDQS
A3.5/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral traits. It only states basic purpose without mentioning rate limits, authentication, dynamic content handling, or error responses.
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?
Single sentence, front-loaded with action, no unnecessary words. Perfectly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description is adequate but lacks details on handling of large pages, timeouts, or what 'text content' entails (e.g., stripping HTML).
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 coverage is 100% (single 'url' parameter described), so baseline is 3. The description adds no extra meaning beyond the schema, such as URL format or protocol support.
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 the action ('Fetch and extract') and the resource ('text content from a webpage'), distinguishing it from sibling tool 'search' which is for querying.
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?
No explicit guidance on when to use this tool versus alternatives. The sibling 'search' suggests a different purpose, but the description does not clarify contexts or exclusions.
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.
2 tool updates
v1.0.0- Added
google_search - Added
read_webpage
TDQS
A3.5/5.0
Scored across 2 tools
Disambiguation5/5
The two tools have clearly distinct purposes: one performs web searches, the other extracts text from a specific URL. There is no overlap or ambiguity.
Naming Consistency5/5
Both tool names follow the same verb_noun pattern with snake_case (google_search, read_webpage), making them predictable and consistent.
Tool Count4/5
With only two tools, the set is minimal but covers the core search workflow. It avoids unnecessary bloat, though additional search variants could be justified.
Completeness4/5
The surface covers the basic search-then-read workflow. Missing features like pagination or filtered searches are minor gaps, but the essential path is complete.
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