A template server implementing the Model Context Protocol (MCP) with OpenAI, Anthropic, and EnrichB2B integration.
- Create a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
- Install dependencies:
pip install -r requirements.txt
- Set up environment variables:
cp .env.example .env
# Edit .env with your API keys and configuration
Development mode:
python server.py
Or using MCP CLI:
mcp dev server.py
- OpenAI GPT-4 integration
- Anthropic Claude integration
- EnrichB2B LinkedIn data integration
- FastAPI and Uvicorn server
- Environment configuration
- Example resources and tools
- Structured project layout
.
├── .env.example # Template for environment variables
├── .gitignore # Git ignore rules
├── README.md # This file
├── requirements.txt # Python dependencies
├── enrichb2b.py # EnrichB2B API client
└── server.py # MCP server implementation
- Start the server
- Connect using any MCP client
- Use the provided tools and resources:
config://app
- Get server configurationget_profile_details
- Get LinkedIn profile informationget_contact_activities
- Get LinkedIn user's recent activities and postsgpt4_completion
- Generate text using GPT-4claude_completion
- Generate text using Claudeanalysis_prompt
- Template for text analysis
Get detailed information about a LinkedIn profile:
result = await get_profile_details(
linkedin_url="https://www.linkedin.com/in/username",
include_company_details=True,
include_followers_count=True
)
Get recent activities and posts from a LinkedIn profile:
result = await get_contact_activities(
linkedin_url="https://www.linkedin.com/in/username",
pages=1, # Number of pages (1-50)
comments_per_post=1, # Comments per post (0-50)
likes_per_post=None # Likes per post (0-50)
)
To add new features:
- Add new tools using the
@mcp.tool()
decorator - Add new resources using the
@mcp.resource()
decorator - Add new prompts using the
@mcp.prompt()
decorator
MIT