LinkedIn Competitor Analysis Server

Overview

A Model Context Protocol (MCP) server that helps you:

• 📊 Monitor and analyze competitor LinkedIn posts
• 💡 Extract insights and trending ideas
• ✍️ Generate original post concepts for your company
• 🎨 Create graphics-ready content with metadata

Features

• Competitor Post Tracking: Fetch and store LinkedIn posts from competitor accounts
• AI-Powered Analysis: Analyze engagement, themes, and content strategies
• Content Generation: Create original post ideas inspired by competitor insights
• Graphics Integration: Export post concepts with metadata for design tools
• Trend Detection: Identify trending topics and content patterns

Tech Stack

• Language: Python 3.10+
• Framework: FastAPI
• LLM Integration: Anthropic Claude API
• LinkedIn API: Official LinkedIn REST APIs
• Database: PostgreSQL (optional, with SQLAlchemy)
• Task Queue: Celery (for async processing)

Project Structure

linkedin-mcp-server/
├── app/
│   ├── __init__.py
│   ├── main.py                 # FastAPI application
│   ├── config.py               # Configuration & environment
│   ├── auth/
│   │   ├── __init__.py
│   │   └── linkedin_auth.py    # LinkedIn OAuth2 flow
│   ├── services/
│   │   ├── __init__.py
│   │   ├── linkedin_service.py # LinkedIn API interactions
│   │   ├── analysis_service.py # AI analysis of posts
│   │   └── content_service.py  # Post generation & formatting
│   ├── models/
│   │   ├── __init__.py
│   │   └── schemas.py          # Pydantic models
│   ├── routes/
│   │   ├── __init__.py
│   │   ├── competitors.py      # Competitor tracking
│   │   ├── posts.py            # Post analysis
│   │   └── generation.py       # Content generation
│   └── utils/
│       ├── __init__.py
│       └── logger.py
├── tests/
│   ├── __init__.py
│   ├── test_linkedin.py
│   └── test_analysis.py
├── .env.example
├── requirements.txt
├── docker-compose.yml
├── Dockerfile
└── README.md

Setup Instructions

Prerequisites

• Python 3.10+
• LinkedIn Developer Account (https://www.linkedin.com/developers/)
• Anthropic API Key (https://console.anthropic.com/)
• PostgreSQL (optional)

1. Clone & Install

git clone https://github.com/satvikjain012-cmyk/linkedin-mcp-server.git
cd linkedin-mcp-server
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

2. Environment Setup

cp .env.example .env

Edit .env with your credentials:

# LinkedIn OAuth
LINKEDIN_CLIENT_ID=your_client_id
LINKEDIN_CLIENT_SECRET=your_client_secret
LINKEDIN_REDIRECT_URI=http://localhost:8000/auth/callback

# Anthropic
ANTHROPIC_API_KEY=your_api_key

# Database (optional)
DATABASE_URL=postgresql://user:password@localhost/linkedin_mcp

# Server
SERVER_HOST=0.0.0.0
SERVER_PORT=8000

3. Run the Server

uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

Server will be available at http://localhost:8000

API Endpoints

Authentication

• GET /auth/linkedin - Initiate LinkedIn OAuth flow
• GET /auth/callback - OAuth callback handler
• POST /auth/logout - Logout and revoke token

Competitors

• POST /competitors - Add competitor to track
• GET /competitors - List tracked competitors
• DELETE /competitors/{id} - Remove competitor

Posts

• GET /posts/competitor/{competitor_id} - Fetch competitor's recent posts
• POST /posts/analyze - Analyze a post or set of posts
• GET /posts/trending - Get trending topics from competitor posts

Content Generation

• POST /generate/post-idea - Generate original post based on competitors
• POST /generate/with-graphics-brief - Generate post with graphics specifications
• GET /generate/history - View generation history

Usage Example

1. Add Competitors to Track

curl -X POST http://localhost:8000/competitors \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer {token}" \
  -d '{
    "name": "Competitor Name",
    "linkedin_url": "https://www.linkedin.com/company/competitor",
    "industry": "Tech"
  }'

2. Fetch & Analyze Their Posts

curl -X GET http://localhost:8000/posts/competitor/1 \
  -H "Authorization: Bearer {token}"

3. Generate Original Content

curl -X POST http://localhost:8000/generate/post-idea \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer {token}" \
  -d '{
    "competitor_ids": [1, 2, 3],
    "topics": ["AI", "automation"],
    "tone": "professional",
    "company_context": "Our company specializes in..."
  }'

4. Generate Post with Graphics Metadata

curl -X POST http://localhost:8000/generate/with-graphics-brief \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer {token}" \
  -d '{
    "post_idea": "generated_post_id",
    "design_preferences": {
      "colors": ["#FF6B6B", "#4ECDC4"],
      "style": "modern",
      "include_stats": true
    }
  }'

Workflow

1. Authenticate with LinkedIn OAuth
   ↓
2. Add competitors you want to track
   ↓
3. Fetch their recent posts (manual or auto-sync)
   ↓
4. AI analyzes posts for:
   - Engagement patterns
   - Content themes
   - Trending topics
   - Audience sentiment
   ↓
5. Generate original post ideas inspired by insights
   ↓
6. Export with graphics specifications (colors, layout, stats)
   ↓
7. Share with design team or graphics tool

Configuration

See config.py for all available settings:

• API rate limiting
• Cache expiration
• LLM model selection
• Post analysis depth

Contributing

Pull requests welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Submit a PR with tests

License

MIT

Support

For issues or questions, open a GitHub issue or contact the maintainer.