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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.

实现模型上下文协议的服务器，使用户能够通过 EnrichB2B API 检索 LinkedIn 个人资料信息和活动数据，并使用 OpenAI GPT-4 或 Anthropic Claude 模型生成文本。

  1. 设置
    1. 运行服务器
      1. 特征
        1. 项目结构
          1. 用法
            1. EnrichB2B 工具
          2. 发展
            1. 执照

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