This Model Context Protocol (MCP) backend component furnishes endpoints for interrogating Google's job postings corpus, facilitated through an integration with the SerpAPI infrastructure. Key features encompass robust internationalization support, granular control over search facets, and sophisticated error mitigation strategies.

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

✨ Capabilities Summary

🌍 Global Language Adaptability

Complete localization support spanning English, Mandarin Chinese, Japanese, and Korean, featuring adaptive language sensing and graceful fallback mechanisms.

🔍 Adaptable Search Facets

Extensive filtering options for job discovery, including: - Role designation and pertinent keywords - Geographic centering with proximity specification - Employment status specification (e.g., permanent, temporary, contract) - Compensation bracket constraints - Posting recency limitations - Result ordering preferences

💡 Intelligent Fault Tolerance

• Rigorous validation of all input parameters
• Provision of constructive diagnostic messages and remediation advice
• Automated suggestions for query refinement
• Mechanisms for managing API rate limitations

📊 Comprehensive Position Data

• Structured formatting of granular employment specifications
• Cataloging of corporate perks and key differentiators
• Presentation of remuneration data when accessible
• Direct hyperlinks for application submission
• Time markers for job publication dates

🔄 Advanced Retrieval Functions

• Support for multi-page result sets (pagination)
• Various result sorting algorithms
• Geographically constrained search radii functionality
• Filtering based on engagement type

🔑 SERP API Credentialing Instructions

Prior to system initialization, securing a SerpAPI access token is mandatory:

1. Navigate to the SERP API portal and establish a user account.

2. Upon account creation, access your administrative console:

3. Identify the segment labeled "API Key"
4. Securely copy your generated credential

5. New accounts customarily receive 100 complimentary lookups.

6. Operational Metrics:

7. Introductory tier: 100 lookups per calendar month
8. Subscription tiers commence at $50 USD monthly for 5000 lookups
9. Billing is predicated on successful data retrievals

10. Accepted settlement methods include major credit cards, PayPal, etc.

11. Constraint Parameters:

12. Inquiry Velocity: 2 requests per second maximum
13. Location Binding: No restrictions imposed
14. Parallel Requests Allowed: Up to 5 concurrently
15. Response Data Caching Duration: 60 minutes

👩‍🔧 Resolution for NVM/NPM-Related Connection Failures in MCP Servers

Reference my documented configuration remedy here 👉 https://github.com/modelcontextprotocol/servers/issues/76

🚀 Rapid Deployment Guide

1. Install prerequisites: bash npm install

2. Environment Configuration: Edit your configuration file, typically named claude_desktop_config.json, incorporating the subsequent structure (ensure path resolution is accurate for your environment):

{ "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. Compile the server application: bash npm run build

2. Initiate the service: bash npm start

Diagnostic Procedures

1. Credential Anomalies:
2. Confirm token validity within configuration file
3. Review token status on the SERP API control panel

4. Verify remaining query quota

5. Search Execution Errors:

6. Scrutinize the format of supplied search variables
7. Examine network stability
8. Confirm supported regional/language identifiers

📦 Acquisition Methods

Automated Installation via Smithery

To integrate the Google Jobs utility for Claude Desktop seamlessly using Smithery:

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

Manual Retrieval

@chanmeng666/google-jobs-server

bash

Using npm package manager

npm i @chanmeng666/google-jobs-server

Alternatively

npm install @chanmeng666/google-jobs-server

Using yarn package manager

yarn add @chanmeng666/google-jobs-server

Using pnpm package manager

pnpm add @chanmeng666/google-jobs-server

Executing Evaluation Routines

The evaluation suite utilizes an embedded mcp client to execute the main entry point (index.ts), eliminating the need for recompilation between test runs. Environment variables can be preloaded using standard prefixing before the npx call. Comprehensive documentation is accessible here.

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

💻 Core Technology Stack

📖 Interface Specification

The server adheres to the Model Context Protocol, presenting a specialized employment search utility with the following argument schema for invocation:

• query: The principal search criteria string (Mandatory field)
• location: Geographical specification for the job hunt (Optional)
• posted_age: Temporal filter for posting date (Optional)
• employment_type: Categorical filter for job status (Optional)
• salary: Numerical filter for compensation expectations (Optional)
• radius: Proximity parameter for location-based searches (Optional)
• hl: Locale identifier for result language (Optional)
• page: Index for result set navigation (Optional)
• sort_by: Criterion for ordering the returned records (Optional)

🔧 Maintenance & Build Commands

bash

Initiate development mode execution

npm run dev

Verify type integrity

npm run typecheck

Generate production artifacts

npm run build

📝 Licensing Information

This software is distributed under the MIT License.

🙋‍♀ Developer Contact

Conceptualized and maintained by Chan Meng.

🤖 AI Agent Integration & Spatial Optimization Details

## AI Companion Utilization Manual This MCP backend service is fine-tuned for seamless integration with autonomous AI agents. Agents can interface with this resource via the subsequent procedures: ### Agent Quick Deployment 1. Installation Command: `npm install @chanmeng666/google-jobs-server` 2. Configuration Step: Inject the server connection settings into the primary MCP client configuration. 3. Execution: Invoke the `search_jobs` function to commence job market reconnaissance. ### Function Argument Reference for AI - `query`: Essential search terms for roles (Mandatory) - `location`: Desired geographic area for employment - `posted_age`: Temporal restriction (e.g., 'today', '3days', 'week', 'month') - `employment_type`: Role categorization (e.g., FULLTIME, PARTTIME, CONTRACTOR, INTERN) - `salary`: Compensation floor expectation (e.g., "$50K+", "$100K+") - `radius`: Search circumference in local units (e.g., '10mi', '20mi', '50mi') - `hl`: Language setting code (e.g., en, zh-CN, ja, ko) - `page`: Result set index (Default provides 10 entries per retrieval) - `sort_by`: Prioritization metric (Options include date, relevance, salary) ### Agent Use Case Scenarios - Assisting users in pinpointing niche professional opportunities - Facilitating job searches localized across various languages - Applying complex filters against available positions - Retrieving comprehensive position data and direct application pathways ### Agent Error Feedback Loop The system furnishes intelligent diagnostic prompts and parameter suggestions to guide agents in optimizing their search queries for better outcomes. ## Machine-Readable Metadata for AI Scanners ## Directives for AI Information Harvesters