🔨 Implement core functionality for Job Search MCP Server, including user profile management, job search, cover letter generation, and Q&A response tools. Add configuration and service layers, and establish dependency management with uv. Introduce .gitignore and .python-version files for environment setup.
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| """Job search tool for finding and ranking relevant jobs - jobs.search endpoint.""" | |
| from typing import Dict, Any | |
| from ..services import JobSearchService | |
| class JobSearchTool: | |
| """Tool for searching and ranking job opportunities.""" | |
| def __init__(self): | |
| self.job_search_service = JobSearchService() | |
| def search( | |
| self, user_id: str, query: str = "", location: str = "", job_type: str = "" | |
| ) -> Dict[str, Any]: | |
| """ | |
| Search for jobs and rank them by relevance to user profile. | |
| This is the main jobs.search endpoint that pulls fresh job posts, | |
| ranks them with GPU embeddings, and returns fit scores. | |
| Args: | |
| user_id: User identifier to get personalized results based on profile | |
| query: Job search query/keywords (e.g., "Python developer", "Data scientist") | |
| location: Preferred job location (e.g., "Remote", "New York", "San Francisco") | |
| job_type: Type of job (e.g., "full-time", "part-time", "contract", "remote", "freelance") | |
| Returns: | |
| Dict with ranked job listings and fit scores: | |
| { | |
| "success": True, | |
| "jobs": [ | |
| { | |
| "id": "job_123", | |
| "title": "Senior Python Developer", | |
| "company": "TechCorp", | |
| "location": "Remote", | |
| "description": "Job description...", | |
| "requirements": "Skills required...", | |
| "salary": "$80,000 - $120,000", | |
| "url": "https://company.com/jobs/123", | |
| "posted_date": "2024-01-15T10:30:00", | |
| "job_type": "full-time", | |
| "fit_score": 85, # Percentage match (0-100) | |
| "match_reasons": ["Skills match: Python, Django", "Location preference match"] | |
| } | |
| ], | |
| "total_found": 10, | |
| "search_params": { | |
| "query": "Python developer", | |
| "location": "Remote", | |
| "job_type": "full-time" | |
| }, | |
| "user_profile": { | |
| "skills_count": 15, | |
| "location": "Remote" | |
| } | |
| } | |
| """ | |
| return self.job_search_service.search_jobs(user_id, query, location, job_type) | |
| def get_job_details(self, job_id: str) -> Dict[str, Any]: | |
| """ | |
| Get detailed information about a specific job. | |
| Args: | |
| job_id: Unique job identifier | |
| Returns: | |
| Dict with detailed job information or error message | |
| """ | |
| # This would typically fetch from job cache or re-scrape | |
| # For now, return a placeholder implementation | |
| return { | |
| "success": False, | |
| "message": "Job details retrieval not implemented yet", | |
| } | |
| def get_search_suggestions(self, user_id: str) -> Dict[str, Any]: | |
| """ | |
| Get personalized job search suggestions based on user profile. | |
| Args: | |
| user_id: User identifier | |
| Returns: | |
| Dict with suggested search queries and parameters | |
| """ | |
| try: | |
| from ..services import ProfileService | |
| profile_service = ProfileService() | |
| profile = profile_service.get_profile(user_id) | |
| if not profile: | |
| return {"success": False, "message": "User profile not found"} | |
| # Generate suggestions based on skills and career goals | |
| suggestions = [] | |
| # Skill-based suggestions | |
| top_skills = profile.skills[:5] # Top 5 skills | |
| for skill in top_skills: | |
| suggestions.append( | |
| { | |
| "query": f"{skill} developer", | |
| "reason": f"Based on your {skill} skills", | |
| } | |
| ) | |
| # Career goal-based suggestions | |
| if "senior" in profile.career_goals.lower(): | |
| suggestions.append( | |
| { | |
| "query": "senior developer", | |
| "reason": "Based on your career goals", | |
| } | |
| ) | |
| if "remote" in profile.career_goals.lower(): | |
| suggestions.append( | |
| { | |
| "location": "Remote", | |
| "reason": "Based on your location preferences", | |
| } | |
| ) | |
| return { | |
| "success": True, | |
| "suggestions": suggestions[:10], # Limit to 10 suggestions | |
| "profile_skills": profile.skills[:10], | |
| } | |
| except Exception as e: | |
| return {"success": False, "message": f"Error getting suggestions: {str(e)}"} | |
| def clear_job_cache(self) -> Dict[str, Any]: | |
| """ | |
| Clear the job search cache to force fresh results. | |
| Returns: | |
| Dict with operation result | |
| """ | |
| try: | |
| self.job_search_service.jobs_cache = {} | |
| self.job_search_service._save_cache() | |
| # Also clear embedding index | |
| self.job_search_service.embedding_service.clear_index() | |
| return { | |
| "success": True, | |
| "message": "Job cache and embeddings cleared successfully", | |
| } | |
| except Exception as e: | |
| return {"success": False, "message": f"Error clearing cache: {str(e)}"} | |