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EnrichB2B MCP Server

by moonlabsai
GitHub
Python
1
  • Linux
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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
  1. Install dependencies:
pip install -r requirements.txt
  1. 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

This server cannot be installed

-
security - not tested
F
license - not found
-
quality - not tested

How are these scores calculated?

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.

Un servidor que implementa el Protocolo de Contexto de Modelo que permite a los usuarios recuperar información del perfil de LinkedIn y datos de actividad a través de la API de EnrichB2B y generar texto utilizando modelos OpenAI GPT-4 o Anthropic Claude.

  1. Configuración
    1. Ejecución del servidor
      1. Características
        1. Estructura del proyecto
          1. Uso
            1. Herramientas EnrichB2B
          2. Desarrollo
            1. Licencia

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