HDW MCP Server: Comprehensive Access to LinkedIn Data and Functionality

Overview: What is HDW MCP Server?

The HDW MCP Server is an advanced Model Context Protocol (MCP) infrastructure that integrates seamlessly with AI applications such as Claude Desktop, Continue, Cursor, and others. This server offers a robust set of APIs for accessing comprehensive LinkedIn data and functionalities through the HorizonDataWave API, making it an essential component in modern AI workflows.

HDW MCP Server provides developers with a versatile toolset to perform various LinkedIn-specific tasks including user search, profile lookup, email lookup, post management, account management (chat functionality, connection management, post commenting), company search & details, and more. The server is designed with a standardized protocol that adheres closely to the Model Context Protocol guidelines, ensuring consistency and ease of integration across different AI applications.

🔧 Core Features & MCP Capabilities

HDW MCP Server offers several key features and capabilities that enhance its value in AI workflows:

User Search

The search_linkedin_users tool enables filtering and searching for LinkedIn users based on various attributes such as keywords, name, title, company, location, industry, and education. This tool supports a wide range of search parameters, allowing developers to fine-tune their queries.

Profile Lookup

Retrieving detailed profile information about a LinkedIn user is facilitated through the get_linkedin_profile tool. Developers can customize which parts of the profile they wish to retrieve (experience, education, skills).

Email Lookup

The get_linkedin_email_user tool provides an email-based approach for finding LinkedIn user details, making it convenient when only an email address is available.

Post & Reaction Retrieval

Tools such as get_linkedin_user_posts, get_linkedin_user_reactions, and their respective reposts and comments tools allow developers to access a user's content directly on the platform.

Account Management

HDW MCP Server supports account management functions like chat, connection requests, and post commenting. These functionalities are crucial for maintaining active social interactions with LinkedIn users through AI-driven applications.

Company Search & Details

Tools such as get_linkedin_google_company and get_linkedin_company enable searching for LinkedIn companies via Google or retrieving detailed information about a specific company by providing its URN.

⚙️ MCP Architecture & Protocol Implementation

The HDW MCP Server implements the Model Context Protocol (MCP) to ensure seamless interoperability with different AI clients. The architecture is designed around a standardized protocol that handles various data exchange formats and methods, making it a versatile choice for developers looking to integrate LinkedIn data into their applications.

MCP Protocol Flow Diagram

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

MCP Client Compatibility Matrix

Below is a compatibility matrix showing the HDW MCP Server’s support for different MCP clients:

MCP Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✅
Continue	✅	✅	✅
Cursor	❌	✅	❌

🚀 Getting Started with Installation

Prerequisites

To get started, you'll need to have a few things ready:

• Access to your HorizonDataWave account.
• An MCP Client such as Claude Desktop, Continue, or Cursor.

Step-by-Step Installation Guide

1. Sign Up and Access Tokens
   Register for an account on the HorizonDataWave platform and obtain your HDW_ACCESS_TOKEN and HDW_ACCOUNT_ID.

2. Create Environment File
   Create a .env file in the root of your project with the following content:

   HDW_ACCESS_TOKEN=YOUR_HD_W_ACCESS_TOKEN
   HDW_ACCOUNT_ID=YOUR_HD_W_ACCOUNT_ID
   

3. Configure MCP Clients

   • For Claude Desktop, update its configuration file (claude_desktop_config.json), as shown in this example:

     {
       "mcpServers": {
         "hdw": {
           "command": "npx",
           "args": ["-y", "@horizondatawave/mcp"],
           "env": {
             "HDW_ACCESS_TOKEN": "YOUR_HD_W_ACCESS_TOKEN",
             "HDW_ACCOUNT_ID": "YOUR_HD_W_ACCOUNT_ID"
           }
         }
       }
     }
     

   • For Cursor, follow these steps:
     1. Open your Cursor settings and add a new MCP server with the command:

        env HDW_ACCESS_TOKEN=your-access-token HDW_ACCOUNT_ID=your-account-id node /path/to/your/build/index.js
        

     2. Or use the provided run.template.sh file to create a dedicated script for improved security and ease of maintenance.

4. Windsurf Configuration
   For Windsurf, update its configuration file (mcp_config.json) as follows:

   {
     "mcpServers": {
       "hdw": {
         "command": "node",
         "args": ["/path/to/your/build/index.js"],
         "env": {
           "HDW_ACCESS_TOKEN": "YOUR_HD_W_ACCESS_TOKEN",
           "HDW_ACCOUNT_ID": "YOUR_HD_W_ACCOUNT_ID"
         }
       }
     }
   }
   

💡 Key Use Cases in AI Workflows

Case Study 1: Talent Recruitment

Imagine an AI application designed to streamline the talent recruitment process. Developers can leverage HDW MCP Server's search_linkedin_users tool to identify potential candidates based on specific criteria and job requirements. Using the get_linkedin_profile tool, recruiters can gather in-depth information about each profile before reaching out. This workflow significantly enhances efficiency by automating the candidate sourcing phase and ensuring a rich data set for initial assessments.

Case Study 2: Social Media Marketing

For marketers looking to reach targeted audiences on LinkedIn, HDW MCP Server provides tools like get_linkedin_user_posts or get_linkedin_company which can be used to analyze user-generated content and campaign effectiveness. By integrating these features into a social media management tool, AI applications can generate insights and optimize marketing strategies based on real-time data.

🔌 Integration with MCP Clients

Example MCP Configuration Code Sample

{
  "mcpServers": {
    "hdw": {
      "command": "npx",
      "args": ["-y", "@horizondatawave/mcp"],
      "env": {
        "HDW_ACCESS_TOKEN": "YOUR_HD_W_ACCESS_TOKEN",
        "HDW_ACCOUNT_ID": "YOUR_HD_W_ACCOUNT_ID"
      }
    }
  }
}

Integrating HDW MCP Server with an AI Application

To integrate the HDW MCP Server into your AI application:

1. Install Dependencies: Ensure you have the necessary dependencies installed via npm.
2. Create Environment Setup: Configure environment variables for accessing tokens and accounts.
3. Update Client Configurations: Customize client configurations to point towards the HDW MCP Server.

📊 Performance & Compatibility Matrix

HDW MCP Server is optimized for performance, ensuring fast data retrieval and efficient processing even under heavy loading conditions. The server supports multiple MCP clients, providing flexibility in integration scenarios.

MCP Client Compatibility

The compatibility matrix below highlights which features are supported by different MCP clients:

MCP Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✅
Continue	✅	✅	✅
Cursor	❌	✅	❌

🛠️ Advanced Configuration & Security

Environment Variables

Ensure that environment variables are correctly set in a .env file:

HDW_ACCESS_TOKEN=YOUR_HD_W_ACCESS_TOKEN
HDW_ACCOUNT_ID=YOUR_HD_W_ACCOUNT_ID

Example Command for Server Execution

To start the MCP server, run the following command:

npx -y @horizondatawave/mcp

❓ Frequently Asked Questions (FAQ)

1. How does HDW MCP Server ensure data privacy? HDW MCP Server adheres to strict data privacy standards and uses secure communication protocols to protect sensitive information.

2. Can I use HDW MCP Server with other AI applications besides those listed? Yes, the server is compatible with any MCP protocol-compliant client. Custom integrations are also supported.

3. Are there any performance trade-offs when using multiple tools simultaneously? The server is designed to handle concurrent requests efficiently, minimizing any potential delays or slowdowns.

4. How can I test the HDW MCP Server’s compatibility with my existing AI application? Use the provided configuration examples as a starting point and modify them according to your application's needs.

5. Is there documentation available for advanced features not covered in this guide? Yes, detailed documentation is available on the official HorizonDataWave website under the developer resources section.

👨‍💻 Development & Contribution Guidelines

Contributions are welcome from developers interested in enhancing HDW MCP Server’s capabilities and functionality. Whether you're fixing bugs, adding new features, or improving existing ones, your contributions can significantly benefit the broader community.

Steps to Contribute

1. Fork the Repository: Clone this repository to your local machine.
2. Set Up Development Environment: Install necessary tools and dependencies as outlined in the README.
3. Create a Pull Request (PR): After completing your work, create a PR detailing your contributions.

🌐 MCP Ecosystem & Resources

The HDW MCP Server is part of a larger ecosystem designed to facilitate easy integration between different AI applications and LinkedIn data sources. Explore additional documentation, tutorials, and community forums for more information on leveraging this server’s full potential.

By integrating HDW MCP Server into your development projects, you can unlock new possibilities in AI workflows, providing enhanced functionality and richer data inputs through direct access to LinkedIn's robust platform.