Google Jobs MCP Server
Una implementación de servidor del Protocolo de Contexto de Modelo (MCP) que proporciona funciones de búsqueda de Google Jobs mediante la integración con SerpAPI. Ofrece compatibilidad con varios idiomas, parámetros de búsqueda flexibles y gestión inteligente de errores.
https://github.com/user-attachments/assets/8f6739e1-7db7-4171-88b4-59c6290a4c72
✨ Características
🌍 Soporte multilingüe
Soporte de localización completo para inglés, chino, japonés y coreano con detección y recuperación automática del idioma.
🔍 Opciones de búsqueda flexibles
Parámetros de búsqueda completos que incluyen:
Título del puesto y palabras clave
Ubicación con filtrado de radio
Tipo de empleo (tiempo completo, tiempo parcial, etc.)
Filtros de rango salarial
Filtrado de fecha de publicación
Ordenación de resultados
💡 Manejo inteligente de errores
Validación de entrada integral
Mensajes de error y sugerencias útiles
Sugerencias automáticas de refinamiento de búsqueda
Manejo del límite de velocidad
📊 Detalles del trabajo enriquecidos
Formato detallado de la información del trabajo
Beneficios y aspectos destacados de la empresa
Información salarial cuando esté disponible
Enlaces directos de aplicaciones
Marcas de tiempo de publicación de empleo
🔄 Funciones avanzadas
Soporte de paginación
Múltiples opciones de clasificación
Búsqueda por radio geográfico
Filtrado por tipo de empleo
Guía de configuración de la API SERP
Antes de comenzar, necesitará obtener una clave API SERP:
Visita el sitio web de SERP API y crea una cuenta
Después de registrarse, vaya a su Panel de Control:
Localice la sección "Clave API"
Copia tu clave API
Los nuevos usuarios obtienen 100 llamadas API gratuitas
Detalles de uso de la API:
Nivel gratuito: 100 búsquedas por mes
Los planes pagos comienzan en $50/mes por 5000 búsquedas
Facturación basada en llamadas API exitosas
Múltiples métodos de pago: Tarjeta de crédito, PayPal, etc.
Límites de uso:
Tasa de solicitud: 2 solicitudes/segundo
Restricciones de IP: Ninguna
Solicitudes concurrentes: 5
Tiempo de caché de respuesta: 1 hora
👩🔧 Solución para problemas de conexión de servidores MCP con NVM/NPM
Haga clic para ver mi solución de configuración 👉 https://github.com/modelcontextprotocol/servers/issues/76
🚀 Inicio rápido
Instalar dependencias:
npm installConfigurar el entorno: Modifique su
claude_desktop_config.jsoncon el siguiente contenido (ajuste las rutas según su sistema):
{
"google-jobs": {
"command": "D:\\Program\\nvm\\node.exe",
"args": ["D:\\github_repository\\path_to\\dist\\index.js"],
"env": {
"SERP_API_KEY": "your-api-key"
}
}
}Construir el servidor:
npm run buildIniciar el servidor:
npm startSolución de problemas
Problemas con la clave API:
Verificar clave en configuración
Comprobar el estado de la clave en el panel de la API de SERP
Confirmar que la clave tiene cuota restante
Problemas de búsqueda:
Validar el formato de los parámetros de búsqueda
Comprobar la conectividad de la red
Verificar la compatibilidad del código de país/idioma
📦 Instalación
Related MCP server: Scrapeless MCP Server
Instalación mediante herrería
Para instalar Google Jobs para Claude Desktop automáticamente a través de Smithery :
npx -y @smithery/cli install @chanmeng666/google-jobs-server --client claudeInstalación manual
@chanmeng666/servidor de empleos de Google
# Using npm
npm i @chanmeng666/google-jobs-server
# or
npm install @chanmeng666/google-jobs-server
# Using yarn
yarn add @chanmeng666/google-jobs-server
# Using pnpm
pnpm add @chanmeng666/google-jobs-serverEjecución de evaluaciones
El paquete evals carga un cliente mcp que ejecuta el archivo index.ts, por lo que no es necesario reconstruir entre pruebas. Puede cargar variables de entorno prefijando el comando npx. Puede encontrar la documentación completa aquí .
OPENAI_API_KEY=your-key npx mcp-eval src/evals/evals.ts src/index.tsPila tecnológica
📖 Documentación de la API
El servidor implementa el Protocolo de Contexto de Modelo y expone una herramienta de búsqueda de empleo con los siguientes parámetros:
query: cadena de consulta de búsqueda (obligatoria)location: Lugar de trabajo (opcional)posted_age: Filtro de fecha de publicación (opcional)employment_type: Filtro de tipo de trabajo (opcional)salary: Filtro de rango salarial (opcional)radius: Radio de búsqueda geográfica (opcional)hl: Código de idioma (opcional)page: Número de paginación (opcional)sort_by: Orden de clasificación (opcional)
🔧 Desarrollo
# Run in development mode
npm run dev
# Run type checking
npm run typecheck
# Build for production
npm run build📝 Licencia
Este proyecto tiene licencia MIT .
🙋♀ Autor
Creado y mantenido por Chan Meng .
Available Tools
1 toolsearch_jobsB
Google Jobs API search tool.
Supported search parameters:
Basic Search: Job title or keywords
Location: City or region
Time Filter: Recently posted jobs
Job Type: Full-time, part-time, contract, internship
Salary Range: Filter by compensation
Geographic Range: Set search radius
Language: Multi-language support
All parameters except 'query' are optional and can be freely combined.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search keywords (Required, e.g., 'software engineer', 'data analyst', 'product manager') | |
| location | No | Job location (Optional, e.g., 'New York', 'London', 'Tokyo') | |
| posted_age | No | Posting date filter (Optional) Options: - "today": Posted today - "3days": Last 3 days - "week": Last week - "month": Last month | |
| employment_type | No | Job type (Optional) Options: - "FULLTIME": Full-time - "PARTTIME": Part-time - "CONTRACTOR": Contractor - "INTERN": Internship - "TEMPORARY": Temporary | |
| salary | No | Salary range (Optional) Format examples: - "$50K+": Above $50,000 - "$100K+": Above $100,000 - "$150K+": Above $150,000 | |
| radius | No | Search radius (Optional) Format examples: - "10mi": Within 10 miles - "20mi": Within 20 miles - "50mi": Within 50 miles | |
| hl | No | Result language (Optional) Options: - "en": English - "zh-CN": Chinese - "ja": Japanese - "ko": Korean | en |
| page | No | Page number (Optional, default: 1) - 10 results per page - Supports pagination | |
| sort_by | No | Sort order (Optional) Options: - "date": Sort by date - "relevance": Sort by relevance - "salary": Sort by salary | relevance |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the search parameters and their optionality, which is useful, but it doesn't mention rate limits, authentication requirements, error handling, or what the output looks like (e.g., format, pagination details beyond '10 results per page' in the schema). For a tool with 9 parameters and no annotations, this leaves significant gaps in understanding its 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 appropriately sized and front-loaded, starting with the tool's purpose and followed by a structured list of parameters. Every sentence adds value, with no redundant information. However, the bulleted list could be slightly more concise, and the final sentence about optional parameters is necessary but adds length.
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 complexity (9 parameters, no output schema, no annotations), the description is partially complete. It covers the search parameters well but lacks details on behavioral aspects like rate limits, authentication, and output format. Without annotations or an output schema, the description should do more to compensate, but it provides a functional overview that is adequate for basic use.
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?
The schema description coverage is 100%, meaning all parameters are well-documented in the input schema itself. The description adds value by summarizing the supported search parameters in a bulleted list and noting their optionality, but it doesn't provide additional semantic context beyond what the schema already covers (e.g., no examples of combined usage). This meets the baseline for high schema coverage.
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 searches for jobs using the Google Jobs API with specific search parameters. It provides a verb ('search') and resource ('jobs'), making the purpose immediately understandable. However, since there are no sibling tools mentioned, it doesn't need to differentiate from alternatives, so a 5 is not warranted.
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 implies usage through the list of supported search parameters and notes that all parameters except 'query' are optional. This provides some context for when to use certain features, but it doesn't offer explicit guidance on when to use this tool versus alternatives (none mentioned) or any prerequisites. The guidance is functional but not strategic.
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_jobs
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'search_jobs' has a clearly defined and distinct purpose for job searching.
The tool name 'search_jobs' follows a consistent verb_noun pattern. Since there is only one tool, there is no inconsistency to evaluate, and the naming is straightforward and descriptive.
A single tool is too few for a server named 'Google Jobs MCP Server', which implies a broader domain of job-related operations. While search is a core function, the lack of tools for actions like retrieving job details, applying, or managing saved jobs makes the set feel incomplete and thin.
The tool surface is severely incomplete for a jobs domain. It only provides search functionality, missing essential operations such as getting detailed job information, applying to jobs, saving or bookmarking jobs, or filtering by employer. This will likely cause agent failures when trying to perform common job-related tasks beyond basic searching.
Maintenance
Related MCP Connectors
A Model Context Protocol server for Wix AI tools
MCP server for Google search results via SERP API
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
Official SerpApi MCP server for Google, Bing, and other search engines.
Related MCP Servers
- AlicenseBqualityDmaintenanceA Model Context Protocol server that enables LLMs to perform web searches using Google's Custom Search API through a standardized interface.147MIT
- AlicenseCqualityCmaintenanceA Model Context Protocol server implementation that enables AI assistants like Claude to perform Google searches and retrieve web data directly through natural language requests.1423 npm169MIT
- FlicenseCqualityCmaintenanceA Model Context Protocol server that enables LLMs to perform Google searches via the Serper API, allowing models to retrieve current information from the web.1339-
- AlicenseNot gradedqualityDmaintenanceA Model Context Protocol server that enables AI assistants to perform web searches using Google Search API, returning up to 20 search results in JSON format.2Apache 2.0