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ChanMeng666

Google Jobs MCP Server

by ChanMeng666
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

一个模型上下文协议 (MCP) 服务器实现，通过 SerpAPI 集成提供 Google 职位搜索功能。它具有多语言支持、灵活的搜索参数和智能错误处理功能。

询问 DeepWiki

👉立即尝试！👈

https://github.com/user-attachments/assets/8f6739e1-7db7-4171-88b4-59c6290a4c72

屏幕截图 2024-12-31 183813

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屏幕截图 2024-12-31 182106

✨ 特点

🌍 多语言支持

完全本地化支持英语、中文、日语和韩语，并具有自动语言检测和回退功能。

🔍 灵活的搜索选项

综合搜索参数包括：

  • 职位名称和关键词

  • 带半径过滤的位置

  • 工作类型（全职、兼职等）

  • 薪资范围过滤器

  • 发布日期过滤

  • 结果排序

💡 智能错误处理

  • 全面的输入验证

  • 有用的错误消息和建议

  • 自动搜索优化建议

  • 速率限制处理

📊 丰富的职位详情

  • 详细职位信息格式

  • 公司福利和亮点

  • 薪资信息（如有）

  • 直接申请链接

  • 职位发布时间戳

🔄 高级功能

  • 分页支持

  • 多种排序选项

  • 地理半径搜索

  • 就业类型过滤

🔑 SERP API 设置指南

在开始之前，您需要获取 SERP API 密钥：

  1. 访问SERP API 网站并创建帐户

  2. 注册后，转到您的仪表板：

    • 找到“API 密钥”部分

    • 复制您的 API 密钥

    • 新用户可获得 100 次免费 API 调用

  3. API 使用详情：

    • 免费套餐：每月 100 次搜索

    • 付费计划起价为每月 50 美元，可进行 5000 次搜索

    • 根据成功的 API 调用计费

    • 多种付款方式：信用卡、PayPal等。

  4. 使用限制：

    • 请求率：每秒 2 个请求

    • IP限制：无

    • 并发请求：5

    • 响应缓存时间：1小时

👩‍🔧 MCP 服务器与 NVM/NPM 连接问题解决方案

点击查看我的配置解决方案👉https: //github.com/modelcontextprotocol/servers/issues/76

🚀 快速入门

  1. 安装依赖项：

npm install

  1. 配置环境：使用以下内容修改您的claude_desktop_config.json （根据您的系统调整路径）：

{
  "google-jobs": {
    "command": "D:\\Program\\nvm\\node.exe",
    "args": ["D:\\github_repository\\path_to\\dist\\index.js"],
    "env": {
      "SERP_API_KEY": "your-api-key"
    }
  }
}

  1. 构建服务器：

npm run build

  1. 启动服务器：

npm start

故障排除

  1. API 密钥问题：

  • 验证配置中的密钥

  • 在 SERP API 仪表板中检查关键状态

  • 确认密钥有剩余配额

  1. 搜索问题：

  • 验证搜索参数格式

  • 检查网络连接

  • 验证国家/语言代码支持

📦安装

Related MCP server: Scrapeless MCP Server

通过 Smithery 安装

要通过Smithery自动为 Claude Desktop 安装 Google Jobs：

npx -y @smithery/cli install @chanmeng666/google-jobs-server --client claude

手动安装

@chanmeng666/google-jobs-server

# 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-server

运行评估

evals 包会加载一个 mcp 客户端，然后运行 index.ts 文件，因此测试之间无需重新构建。您可以通过在 npx 命令前添加前缀来加载环境变量。完整文档可在此处找到。

OPENAI_API_KEY=your-key  npx mcp-eval src/evals/evals.ts src/index.ts

💻 技术栈

TypeScriptNodeJS微胶囊钙

📖 API 文档

服务器实现了模型上下文协议，并公开了一个具有以下参数的求职工具：

  • query ：搜索查询字符串（必需）

  • location ：工作地点（可选）

  • posted_age ：发布日期过滤器（可选）

  • employment_type ：工作类型过滤器（可选）

  • salary ：薪资范围过滤器（可选）

  • radius ：地理搜索半径（可选）

  • hl ：语言代码（可选）

  • page ：页码（可选）

  • sort_by ：排序顺序（可选）

🔧 开发

# Run in development mode
npm run dev

# Run type checking
npm run typecheck

# Build for production
npm run build

📝 许可证

该项目已获得 MIT 许可。

🙋‍♀ 作者

由Chan Meng创建并维护。 GitHub LinkedIn

Available Tools

1 tool
search_jobsB

Google Jobs API search tool.

Supported search parameters:

  1. Basic Search: Job title or keywords

  2. Location: City or region

  3. Time Filter: Recently posted jobs

  4. Job Type: Full-time, part-time, contract, internship

  5. Salary Range: Filter by compensation

  6. Geographic Range: Set search radius

  7. Language: Multi-language support

All parameters except 'query' are optional and can be freely combined.

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYesSearch keywords (Required, e.g., 'software engineer', 'data analyst', 'product manager')
locationNoJob location (Optional, e.g., 'New York', 'London', 'Tokyo')
posted_ageNoPosting date filter (Optional) Options: - "today": Posted today - "3days": Last 3 days - "week": Last week - "month": Last month
employment_typeNoJob type (Optional) Options: - "FULLTIME": Full-time - "PARTTIME": Part-time - "CONTRACTOR": Contractor - "INTERN": Internship - "TEMPORARY": Temporary
salaryNoSalary range (Optional) Format examples: - "$50K+": Above $50,000 - "$100K+": Above $100,000 - "$150K+": Above $150,000
radiusNoSearch radius (Optional) Format examples: - "10mi": Within 10 miles - "20mi": Within 20 miles - "50mi": Within 50 miles
hlNoResult language (Optional) Options: - "en": English - "zh-CN": Chinese - "ja": Japanese - "ko": Koreanen
pageNoPage number (Optional, default: 1) - 10 results per page - Supports pagination
sort_byNoSort order (Optional) Options: - "date": Sort by date - "relevance": Sort by relevance - "salary": Sort by salaryrelevance

TDQS

B3.2/5.0
Behavior2/5

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.

Conciseness4/5

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.

Completeness3/5

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.

Parameters3/5

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.

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 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.

Usage Guidelines3/5

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. 1 tool updatev1.0.0
    • First observedsearch_jobs
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 across 1 tool

Disambiguation5/5

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.

Naming Consistency5/5

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.

Tool Count2/5

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.

Completeness2/5

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

ActivityInactive
ResponsivenessNo issues

Resources

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