Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@LinkedIn Competitor Analysis MCP Server Analyze my top competitor's last 5 posts"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server that helps you:
š Monitor and analyze competitor LinkedIn posts
š” Extract insights and trending ideas
āļø Generate original post concepts for your company
šØ Create graphics-ready content with metadata
Related MCP server: LinkedIn Content Creation MCP Server
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
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)
linkedin-mcp-server/
āāā app/
ā āāā __init__.py
ā āāā main.py # FastAPI application
ā āāā config.py # Configuration & environment
ā āāā auth/
ā ā āāā __init__.py
ā ā āāā linkedin_auth.py # LinkedIn OAuth2 flow
ā āāā services/
ā ā āāā __init__.py
ā ā āāā linkedin_service.py # LinkedIn API interactions
ā ā āāā analysis_service.py # AI analysis of posts
ā ā āāā content_service.py # Post generation & formatting
ā āāā models/
ā ā āāā __init__.py
ā ā āāā schemas.py # Pydantic models
ā āāā routes/
ā ā āāā __init__.py
ā ā āāā competitors.py # Competitor tracking
ā ā āāā posts.py # Post analysis
ā ā āāā generation.py # Content generation
ā āāā utils/
ā āāā __init__.py
ā āāā logger.py
āāā tests/
ā āāā __init__.py
ā āāā test_linkedin.py
ā āāā test_analysis.py
āāā .env.example
āāā requirements.txt
āāā docker-compose.yml
āāā Dockerfile
āāā README.mdPython 3.10+
LinkedIn Developer Account (https://www.linkedin.com/developers/)
Anthropic API Key (https://console.anthropic.com/)
PostgreSQL (optional)
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.txtcp .env.example .envEdit .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=8000uvicorn app.main:app --reload --host 0.0.0.0 --port 8000Server will be available at http://localhost:8000
GET /auth/linkedin - Initiate LinkedIn OAuth flow
GET /auth/callback - OAuth callback handler
POST /auth/logout - Logout and revoke token
POST /competitors - Add competitor to track
GET /competitors - List tracked competitors
DELETE /competitors/{id} - Remove competitor
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
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
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"
}'curl -X GET http://localhost:8000/posts/competitor/1 \
-H "Authorization: Bearer {token}"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..."
}'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
}
}'1. Authenticate with LinkedIn OAuth
ā
2. Add competitors you want to track
ā
3. Fetch their recent posts (manual or auto-sync)
ā
4. AI analyzes posts for:
- Engagement patterns
- Content themes
- Trending topics
- Audience sentiment
ā
5. Generate original post ideas inspired by insights
ā
6. Export with graphics specifications (colors, layout, stats)
ā
7. Share with design team or graphics toolSee config.py for all available settings:
API rate limiting
Cache expiration
LLM model selection
Post analysis depth
Pull requests welcome! Please:
Fork the repository
Create a feature branch
Submit a PR with tests
MIT
For issues or questions, open a GitHub issue or contact the maintainer.
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