EnrichB2B MCP Server

MCP Template Server

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

Setup

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

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

Start the server
Connect using any MCP client
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:

Add new tools using the @mcp.tool() decorator
Add new resources using the @mcp.resource() decorator
Add new prompts using the @mcp.prompt() decorator

License

MIT

This server cannot be installed

security - not tested

license - not found

quality - not tested

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

A server implementing the Model Context Protocol that enables users to retrieve LinkedIn profile information and activity data via EnrichB2B API, and generate text using OpenAI GPT-4 or Anthropic Claude models.

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