MCP Server POC - AI-Powered Resume-to-Job Matching

A Spring Boot-based Model Context Protocol (MCP) server that provides intelligent resume analysis and job matching capabilities using AI embeddings and the JSearch API.

🎯 Overview

This MCP server enables AI assistants (like Claude Desktop, Cline) to:

• Upload and analyze resumes - Extract structured information using LLM
• Store resume embeddings - Vector-based storage for semantic search
• Match resumes to jobs - Find relevant job postings using JSearch API (RapidAPI)
• Rank job matches - Score jobs based on semantic similarity and filters

🏗️ Architecture

┌─────────────────────┐
│  Claude Desktop /   │
│  Cline (MCP Client) │
└──────────┬──────────┘
           │ SSE/STDIO
           ▼
┌─────────────────────┐
│  MCP Server         │
│  (Spring Boot)      │
├─────────────────────┤
│  • ResumeTools      │
│  • MatchingTools    │
│  • CourseService    │
└──────────┬──────────┘
           │
    ┌──────┴──────┐
    ▼             ▼
┌─────────┐  ┌──────────┐
│ Vector  │  │ JSearch  │
│ Store   │  │ API      │
│(In-Mem) │  │(RapidAPI)│
└─────────┘  └──────────┘

📦 Components

MCP Tools

• upload_resume - Process resume text and generate structured summary
• get_resume_summary - Retrieve stored resume summary by ID
• match_resume_to_jobs - Find and rank matching jobs from JSearch API
• getAllCourse - Demo tool to list available courses
• getCourseById - Demo tool to get course by title
• print_env - Diagnostic tool to print environment variables
• llm_diagnostics - Diagnostic tool for LLM configuration and health check

Services

• ResumeService - Resume processing and LLM-based extraction
• VectorStoreService - In-memory vector storage with cosine similarity
• EmbeddingService - Text-to-vector embedding (stub implementation)
• JSearchService - Integration with JSearch API for job listings
• LlmClient - LLM interface for chat completions

🚀 Getting Started

Prerequisites

• Java 21+
• Gradle 8.14.3+
• RapidAPI account with JSearch API access
• Claude Desktop or Cline (VS Code extension)

Step 1: Clone and Setup

git clone <repository-url>
cd job-recommendations-mcp-server

Step 2: Configure API Keys

1. Copy the example environment file:

copy .env.example .env

2. Edit .env and add your RapidAPI key:

JSEARCH_API_KEY=your_actual_rapidapi_key_here
JSEARCH_API_HOST=jsearch.p.rapidapi.com

Get your API key from: https://rapidapi.com/letscrape-6bRBa3QguO5/api/jsearch

Step 3: Build the Project

# Windows
gradlew.bat clean bootJar

# Unix/Linux/Mac
./gradlew clean bootJar

The JAR will be created at: build/libs/job-recommendations-mcp-server-0.0.1.jar

Step 4: Choose Transport Mode

Option A: STDIO (Local Development)

Configuration:

• Comment out SSE config in application.properties
• Uncomment: spring.main.web-application-type=none

Claude Desktop Config (claude_desktop_config.json):

{
  "mcpServers": {
    "resume-matcher": {
      "command": "java",
      "args": [
        "-jar",
        "C:\\Workspace\\mcp\\job-recommendations-mcp-server\\build\\libs\\job-recommendations-mcp-server-0.0.1.jar"
      ]
    }
  }
}

Option B: SSE (Production Ready)

Configuration:

• Keep current application.properties (SSE enabled)
• Server runs on http://localhost:8080

Run the server:

java -jar build/libs/job-recommendations-mcp-server-0.0.1.jar

Claude Desktop Config:

{
  "mcpServers": {
    "resume-matcher": {
      "url": "http://localhost:8080/sse"
    }
  }
}

For Cline (VS Code): Add to VS Code settings.json:

{
  "cline.mcpServers": {
    "resume-matcher": {
      "url": "http://localhost:8080/sse"
    }
  }
}

Step 5: Restart MCP Client

• Claude Desktop: Restart the application
• Cline: Reload VS Code window

💡 Usage Exam

... View full README on GitHub