MCP Template Server

A template server implementing the Model Context Protocol (MCP) with OpenAI, Anthropic, and EnrichB2B integration.

Setup

1. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install dependencies:

pip install -r requirements.txt

3. Set up environment variables:

cp .env.example .env
# Edit .env with your API keys and configuration

Running the Server

Development mode:

python server.py

Or using MCP CLI:

mcp dev server.py

Features

• OpenAI GPT-4 integration
• Anthropic Claude integration
• EnrichB2B LinkedIn data integration
• FastAPI and Uvicorn server
• Environment configuration
• Example resources and tools
• Structured project layout

Project Structure

.
├── .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

Usage

1. Start the server

2. Connect using any MCP client

3. Use the provided tools and resources:

EnrichB2B Tools

get_profile_details

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_contact_activities

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)
)

Development

To add new features:

1. Add new tools using the `@mcp.tool()` decorator

2. Add new resources using the `@mcp.resource()` decorator

3. Add new prompts using the `@mcp.prompt()` decorator

License

MIT