🚀 Initial Boilerplate setup

app.py

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+import gradio as gr
+import json
+from typing import Dict, List, Any
+
+
+class JobSearchMCP:
+    """Job Search MCP Server - Smart job matching and application helper"""
+
+    def __init__(self):
+        self.user_profiles = {}  # In-memory storage for demo purposes
+
+    def profile_upsert(self, user_id: str, profile_data: str) -> Dict[str, Any]:
+        """
+        Stores/updates user résumé, skills, salary expectations, and career goals
+
+        Args:
+            user_id: Unique identifier for the user
+            profile_data: JSON string containing user profile information
+
+        Returns:
+            Dict with success status and message
+        """
+        try:
+            # TODO: Implement profile storage logic
+            # - Parse profile_data JSON
+            # - Validate required fields (resume, skills, salary_wish, career_goals)
+            # - Store in database or persistent storage
+            # - Return success/error response
+
+            return {
+                "success": True,
+                "message": "Profile updated successfully",
+                "user_id": user_id,
+            }
+        except Exception as e:
+            return {"success": False, "message": f"Error updating profile: {str(e)}"}
+
+    def jobs_search(
+        self, user_id: str, query: str = "", location: str = "", job_type: str = ""
+    ) -> Dict[str, Any]:
+        """
+        Pulls fresh job posts, ranks them with GPU embeddings, and returns fit scores
+
+        Args:
+            user_id: User identifier to get personalized results
+            query: Job search query/keywords
+            location: Preferred job location
+            job_type: Type of job (full-time, contract, remote, etc.)
+
+        Returns:
+            Dict with ranked job listings and fit scores
+        """
+        try:
+            # TODO: Implement job search logic
+            # - Fetch jobs from various APIs (LinkedIn, Indeed, etc.)
+            # - Use GPU embeddings to calculate job-profile fit scores
+            # - Rank jobs by relevance and fit score
+            # - Return top matches with metadata
+
+            return {
+                "success": True,
+                "jobs": [],
+                "total_found": 0,
+                "search_params": {
+                    "query": query,
+                    "location": location,
+                    "job_type": job_type,
+                },
+            }
+        except Exception as e:
+            return {"success": False, "message": f"Error searching jobs: {str(e)}"}
+
+    def letter_generate(
+        self, user_id: str, job_description: str, tone: str = "professional"
+    ) -> Dict[str, Any]:
+        """
+        Generates personalized cover letters using LLM
+
+        Args:
+            user_id: User identifier to access profile
+            job_description: The job posting description
+            tone: Tone of the cover letter (professional, casual, enthusiastic, etc.)
+
+        Returns:
+            Dict with generated cover letter
+        """
+        try:
+            # TODO: Implement cover letter generation
+            # - Retrieve user profile
+            # - Use LLM to generate personalized cover letter
+            # - Match user skills with job requirements
+            # - Apply specified tone
+            # - Return formatted cover letter
+
+            return {
+                "success": True,
+                "cover_letter": "",
+                "tone_used": tone,
+                "word_count": 0,
+            }
+        except Exception as e:
+            return {
+                "success": False,
+                "message": f"Error generating cover letter: {str(e)}",
+            }
+
+    def qa_reply(
+        self, user_id: str, question: str, context: str = ""
+    ) -> Dict[str, Any]:
+        """
+        Drafts concise answers to client questions
+
+        Args:
+            user_id: User identifier to access profile
+            question: The question from potential employer/client
+            context: Additional context about the conversation
+
+        Returns:
+            Dict with generated response
+        """
+        try:
+            # TODO: Implement Q&A response generation
+            # - Retrieve user profile and experience
+            # - Generate contextual response using LLM
+            # - Keep response concise and professional
+            # - Tailor to user's background and skills
+
+            return {"success": True, "response": "", "confidence_score": 0.0}
+        except Exception as e:
+            return {"success": False, "message": f"Error generating response: {str(e)}"}
+
+
+# Initialize the MCP server
+mcp_server = JobSearchMCP()
+
+# Create Gradio interface for each endpoint
+with gr.Blocks(title="Job Search MCP Server") as demo:
+    gr.Markdown("# 🔍 Job Search MCP Server")
+    gr.Markdown("Smart job matching and instant application helper")
+
+    with gr.Tab("Profile Management"):
+        gr.Markdown("### Store and update your professional profile")
+        with gr.Row():
+            profile_user_id = gr.Textbox(
+                label="User ID", placeholder="Enter your unique user ID"
+            )
+            profile_data = gr.TextArea(
+                label="Profile Data (JSON)",
+                placeholder='{"resume": "...", "skills": [...], "salary_wish": "...", "career_goals": "..."}',
+                lines=5,
+            )
+        profile_submit = gr.Button("Update Profile", variant="primary")
+        profile_output = gr.JSON(label="Response")
+
+        profile_submit.click(
+            fn=mcp_server.profile_upsert,
+            inputs=[profile_user_id, profile_data],
+            outputs=profile_output,
+        )
+
+    with gr.Tab("Job Search"):
+        gr.Markdown("### Find and rank relevant job opportunities")
+        with gr.Row():
+            search_user_id = gr.Textbox(label="User ID")
+            search_query = gr.Textbox(
+                label="Search Query", placeholder="e.g., Python developer"
+            )
+        with gr.Row():
+            search_location = gr.Textbox(
+                label="Location", placeholder="e.g., Remote, New York"
+            )
+            search_job_type = gr.Dropdown(
+                label="Job Type",
+                choices=["full-time", "part-time", "contract", "freelance", "remote"],
+                value="full-time",
+            )
+        search_submit = gr.Button("Search Jobs", variant="primary")
+        search_output = gr.JSON(label="Job Results")
+
+        search_submit.click(
+            fn=mcp_server.jobs_search,
+            inputs=[search_user_id, search_query, search_location, search_job_type],
+            outputs=search_output,
+        )
+
+    with gr.Tab("Cover Letter Generator"):
+        gr.Markdown("### Generate personalized cover letters")
+        with gr.Row():
+            letter_user_id = gr.Textbox(label="User ID")
+            letter_tone = gr.Dropdown(
+                label="Tone",
+                choices=["professional", "casual", "enthusiastic", "formal"],
+                value="professional",
+            )
+        letter_job_desc = gr.TextArea(
+            label="Job Description",
+            placeholder="Paste the job description here...",
+            lines=5,
+        )
+        letter_submit = gr.Button("Generate Cover Letter", variant="primary")
+        letter_output = gr.JSON(label="Generated Letter")
+
+        letter_submit.click(
+            fn=mcp_server.letter_generate,
+            inputs=[letter_user_id, letter_job_desc, letter_tone],
+            outputs=letter_output,
+        )
+
+    with gr.Tab("Q&A Assistant"):
+        gr.Markdown("### Get help with interview questions and client responses")
+        with gr.Row():
+            qa_user_id = gr.Textbox(label="User ID")
+            qa_context = gr.Textbox(
+                label="Context (optional)", placeholder="Additional context..."
+            )
+        qa_question = gr.TextArea(
+            label="Question", placeholder="e.g., Why should we hire you?", lines=3
+        )
+        qa_submit = gr.Button("Generate Response", variant="primary")
+        qa_output = gr.JSON(label="Generated Response")
+
+        qa_submit.click(
+            fn=mcp_server.qa_reply,
+            inputs=[qa_user_id, qa_question, qa_context],
+            outputs=qa_output,
+        )
+
+if __name__ == "__main__":
+    # Enable MCP server functionality
+    demo.launch(enable_mcp=True)

config.py

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+import os
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+
+class Config:
+    """Configuration settings for Job Search MCP Server"""
+
+    # API Keys
+    OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+    ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY")
+    HUGGINGFACE_API_KEY = os.getenv("HUGGINGFACE_API_KEY")
+
+    # Job Search APIs
+    LINKEDIN_API_KEY = os.getenv("LINKEDIN_API_KEY")
+    INDEED_API_KEY = os.getenv("INDEED_API_KEY")
+    GLASSDOOR_API_KEY = os.getenv("GLASSDOOR_API_KEY")
+
+    # Database Settings (for future implementation)
+    DATABASE_URL = os.getenv("DATABASE_URL", "sqlite:///jobsearch.db")
+
+    # Embedding Model Settings
+    EMBEDDING_MODEL = os.getenv(
+        "EMBEDDING_MODEL", "sentence-transformers/all-MiniLM-L6-v2"
+    )
+    EMBEDDING_CACHE_SIZE = int(os.getenv("EMBEDDING_CACHE_SIZE", "1000"))
+
+    # LLM Settings
+    DEFAULT_LLM_MODEL = os.getenv("DEFAULT_LLM_MODEL", "gpt-3.5-turbo")
+    MAX_TOKENS = int(os.getenv("MAX_TOKENS", "1000"))
+    TEMPERATURE = float(os.getenv("TEMPERATURE", "0.7"))
+
+    # Job Search Settings
+    MAX_JOBS_PER_SEARCH = int(os.getenv("MAX_JOBS_PER_SEARCH", "50"))
+    MIN_MATCH_SCORE = float(os.getenv("MIN_MATCH_SCORE", "0.6"))
+
+    # Cover Letter Settings
+    MAX_COVER_LETTER_WORDS = int(os.getenv("MAX_COVER_LETTER_WORDS", "300"))
+
+    # Server Settings
+    SERVER_HOST = os.getenv("SERVER_HOST", "127.0.0.1")
+    SERVER_PORT = int(os.getenv("SERVER_PORT", "7860"))
+    DEBUG = os.getenv("DEBUG", "False").lower() == "true"
+
+    @classmethod
+    def validate_config(cls):
+        """Validate that required configuration is present"""
+        required_keys = ["OPENAI_API_KEY", "ANTHROPIC_API_KEY"]
+
+        missing_keys = []
+        for key in required_keys:
+            if not getattr(cls, key):
+                missing_keys.append(key)
+
+        if missing_keys:
+            raise ValueError(
+                f"Missing required environment variables: {', '.join(missing_keys)}"
+            )
+
+        return True

requirements.txt

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+gradio[mcp]>=5.0.0
+openai>=1.0.0
+anthropic>=0.30.0
+requests>=2.31.0
+python-dotenv>=1.0.0
+numpy>=1.24.0
+sentence-transformers>=2.2.0
+scikit-learn>=1.3.0
+pandas>=2.0.0
+beautifulsoup4>=4.12.0
+lxml>=4.9.0
+httpx>=0.24.0
+pydantic>=2.0.0
+python-multipart>=0.0.6