What is Enrich B2b Mcp

enrich_b2b_mcp is a template server that implements the Model Context Protocol (MCP) and integrates with OpenAI, Anthropic, and EnrichB2B services, enabling advanced data processing and AI functionalities.

Use cases

Use cases include retrieving detailed LinkedIn profile information, accessing recent activities of LinkedIn users, generating text completions using GPT-4 or Claude, and performing text analysis.

How to use

To use enrich_b2b_mcp, set up a virtual environment, install the required dependencies, configure environment variables with your API keys, and run the server in development mode. Connect using any MCP client to access its features.

Key features

Key features include integration with OpenAI GPT-4, Anthropic Claude, EnrichB2B LinkedIn data, a FastAPI and Uvicorn server, environment configuration, and structured project layout with example resources.

Where to use

enrich_b2b_mcp can be used in various fields such as business intelligence, data enrichment, customer relationship management, and any application requiring advanced AI-driven data analysis and processing.

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:
   • config://app - Get server configuration
   • get_profile_details - Get LinkedIn profile information
   • get_contact_activities - Get LinkedIn user’s recent activities and posts
   • gpt4_completion - Generate text using GPT-4
   • claude_completion - Generate text using Claude
   • analysis_prompt - Template for text analysis

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