Careerlens MCP
MCP server that gives Claude real-time career intelligence, salary benchmarks, live job listings, skill demand analysis & resume ATS scoring. Built with FastMCP + FastAPI + React. Claude Desktop compatible.
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β‘ CareerLens MCP
Real-time career intelligence as an MCP server. Ask Claude if you're underpaid, find live job listings, benchmark your skills, and score your resume β all in one chat.

Live Demo β Β |Β Backend API Docs β
What It Does
CareerLens exposes 5 career intelligence tools via the Model Context Protocol (MCP). Claude decides which tools to call based on your question β you just chat.
| Tool | What it answers |
|---|---|
salary_benchmark | What should I be earning for this role? |
job_search | What's actually hiring for my skills right now? |
skill_demand | Is my tech stack trending up or dying? |
resume_fit_score | How well does my resume match this job? |
career_report | Full briefing: salary gap + live jobs + skill rankings |
Bonus features:
- π Upload your CV (PDF) β auto-extracts your profile
- π Paste your LinkedIn URL β auto-fills your profile
Architecture
React Frontend (Vercel)
β
β POST /chat β SSE streaming
βΌ
FastAPI Bridge Server (Railway)
β
βββ Anthropic SDK (Claude Sonnet)
β β tool_use (agentic loop)
β βΌ
βββ tools/*.py βββΊ JSearch API (LinkedIn, Indeed, Glassdoor)
βββΊ Pure Python (resume ATS scoring)
Claude Desktop (optional)
β stdio / MCP protocol
βΌ
FastMCP Server βββΊ same tools/*.py
The MCP server and the API server share the same tool implementations in server/tools/. Zero duplication.
Tech Stack
| Layer | Tech |
|---|---|
| MCP Server | Python + FastMCP |
| API Server | Python + FastAPI + SSE streaming |
| Job/Salary Data | JSearch API (LinkedIn, Indeed, Glassdoor) |
| AI | Claude Sonnet via Anthropic SDK |
| Frontend | React + Tailwind CSS |
| Backend Deploy | Railway |
| Frontend Deploy | Vercel |
Quick Start
Prerequisites
- Python 3.11+
- Node.js 18+
- JSearch API key (free tier)
- Anthropic API key
1. Clone the repo
git clone https://github.com/yourusername/careerlens-mcp.git
cd careerlens-mcp
2. Backend setup
cd server
pip install -r requirements.txt
cp .env.example .env
# Edit .env and add your API keys
uvicorn api_server:app --reload --port 8000
3. Frontend setup
cd client
npm install
cp .env.example .env.local
# .env.local already points to localhost:8000
npm start
Open http://localhost:3000 β fill in your profile and start chatting.
4. Claude Desktop setup (optional)
Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"careerlens": {
"command": "python",
"args": ["/absolute/path/to/careerlens-mcp/server/mcp_server.py"],
"env": {
"JSEARCH_API_KEY": "your_key",
"ANTHROPIC_API_KEY": "your_key"
}
}
}
}
Deploy Your Own
Backend β Railway
- Create new project on railway.app
- Connect your GitHub repo, set Root Directory to
server - Add environment variables:
JSEARCH_API_KEY,ANTHROPIC_API_KEY - Railway auto-deploys from
Procfile
Frontend β Vercel
- Import repo on vercel.com
- Set Root Directory to
client - Add environment variable:
REACT_APP_API_URL=https://your-app.railway.app - Deploy
Project Structure
careerlens-mcp/
βββ server/
β βββ tools/
β β βββ __init__.py
β β βββ salary.py # Tool 1: Salary benchmarks
β β βββ jobs.py # Tool 2: Live job search
β β βββ skills.py # Tool 3: Skill demand analysis
β β βββ resume.py # Tool 4: ATS resume scoring
β β βββ report.py # Tool 5: Full career report
β β βββ cv_parser.py # CV PDF parsing
β β βββ linkedin_parser.py # LinkedIn profile scraping
β βββ api_server.py # FastAPI + SSE bridge for frontend
β βββ mcp_server.py # FastMCP server for Claude Desktop
β βββ config.py
β βββ requirements.txt
β βββ Procfile
β βββ railway.toml
βββ client/
β βββ src/
β β βββ App.js # Full chat UI + result cards
β β βββ App.css # Dark bold theme
β βββ vercel.json
β βββ package.json
βββ .gitignore
βββ README.md
Built With
Built in a weekend as a demonstration of the Model Context Protocol (MCP) β the open standard for connecting AI models to external tools and data sources.
License
MIT β do whatever you want with it.
