EnrichB2B MCP Server
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.
README
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 configurationget_profile_details- Get LinkedIn profile informationget_contact_activities- Get LinkedIn user's recent activities and postsgpt4_completion- Generate text using GPT-4claude_completion- Generate text using Claudeanalysis_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
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.