LinkedInMCP MCP Server: Revolutionizing LinkedIn API Interactions

Overview: What is LinkedInMCP MCP Server?

LinkedInMCP is an innovative Model Context Protocol (MCP) server designed to revolutionize how developers interact with LinkedIn's API by providing a robust, extensible framework for advanced LinkedIn data retrieval and interaction. This project fills the gap between traditional static LinkedIn APIs and more flexible modern AI applications, offering a standardized protocol that allows AI applications like Claude Desktop, Continue, Cursor, and others to seamlessly integrate with LinkedIn through MCP.

🔧 Core Features & MCP Capabilities

LinkedInMCP leverages Model Context Protocol (MCP) to create an intelligent, context-aware method of working with professional network data. The core capabilities include:

1. Advanced People Search: Users can search for individuals on LinkedIn based on keywords, current company, industries, and location.
2. Detailed Profile Retrieval: Retrieve comprehensive information about LinkedIn profiles in a structured format.
3. Job Market Intelligence: Gain insights into the job market through sophisticated filtering and data analysis.
4. Secure, Typed Interactions: Provide type-safe interactions with LinkedIn's API to ensure security and reliability.

The MCP protocol flow ensures a secure and efficient connection between AI applications and LinkedIn by enabling intelligent context management and providing standardized interactions.

⚙️ MCP Architecture & Protocol Implementation

LinkedInMCP is built using TypeScript and implements the Model Context Protocol (MCP) to provide a robust and extensible framework for interacting with LinkedIn. Key components include:

• Authentication: Implements OAuth 2.0 to securely authenticate users and manage token refreshes.
• Data Storage: Minimal data storage with selective field return, ensuring privacy and security.
• API Interactions: Uses Axios for HTTP requests to interact with the LinkedIn API.

The project structure includes both server-side and client-side components, allowing for flexible integration and customization.

🚀 Getting Started with Installation

To get started with LinkedInMCP, follow these steps:

1. Prerequisites:

   • Node.js 16+
   • LinkedIn Developer Account
   • MCP-Compatible LLM (Claude Desktop, Continue, Cursor, etc.)

2. Project Setup:

   git clone https://github.com/Dishant27/linkedin-mcp-server.git
   cd linkedin-mcp-server
   npm install
   

3. Configuration Files:

   • .env File: Create a .env file in the project root and set up your LinkedIn Client ID and Secret.

     LINKEDIN_CLIENT_ID=your_client_id_here
     LINKEDIN_CLIENT_SECRET=your_client_secret_here
     

   • claude_desktop_config.json (for Claude Desktop):

     {
       "mcpServers": {
         "linkedin": {
           "command": "node",
           "args": ["/absolute/path/to/linkedin-mcp-server/dist/index.js"],
           "env": {
             "LINKEDIN_CLIENT_ID": "your_client_id",
             "LINKEDIN_CLIENT_SECRET": "your_client_secret"
           }
         }
       }
     }
     

4. Start the MCP Server:

   npm run build
   npm start
   

5. LLM Configuration Example:

   {
     "tools": [
       {
         "name": "search-people",
         "description": "Search for LinkedIn profiles",
         "parameters": {
           "type": "object",
           "properties": {
             "keywords": { "type": "string", "description": "Keywords to search for in profiles" },
             "currentCompany": { "type": "array", "items": {"type": "string"}, "description": "Filter by current company" },
             "industries": { "type": "array", "items": {"type": "string"}, "description": "Filter by industries" },
             "location": { "type": "string",  "description": "Filter by location" }
           }
         }
       },
       {
         "name": "get-profile",
         "description": "Retrieve detailed LinkedIn profile information",
         "parameters": {
           "type": "object",
           "properties": {
             "publicId": { "type": "string",  "description": "Public ID of the LinkedIn profile" },
             "urnId": { "type": "string", "description": "URN ID of the LinkedIn profile" }
           }
         }
       }
     ]
   }
   

💡 Key Use Cases in AI Workflows

Talent Acquisition

• Candidate Sourcing: Find qualified candidates based on specific skill and experience criteria.
• Talent Pool Analysis: Map talent distribution across industries and geographies.
• Outreach Automation: Streamline initial contact with potential candidates.
• Competitive Intelligence: Understand recruitment patterns at competitor organizations.

Sales & Business Development

• Lead Generation: Identify decision-makers at target companies.
• Account-Based Marketing: Build comprehensive profiles of key accounts.
• Relationship Mapping: Visualize connection networks within organizations.
• Market Penetration Analysis: Track industry adoption of products or services.

🔌 Integration with MCP Clients

The LinkedInMCP server is compatible with various MCP clients, including:

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

This compatibility matrix indicates full support for resources, tools, and prompts in Claude Desktop and Continue.

📊 Performance & Compatibility Matrix

Realistic AI Workflow Use Cases:

Use Case 1: Talent Acquisition

• Scenario: An HR professional needs to find suitable candidates for a project.
• Technical Implementation:
  • The MCP server is set up with the necessary environment variables and configuration files.
  • An AI application, such as Continue, sends a request via the MCP protocol to search-profiles based on specific keywords like "React.js" in San Francisco.

Use Case 2: Sales & Business Development

• Scenario: A business developer needs detailed information about key accounts within a target industry.
• Technical Implementation:
  • The MCP server is configured with the required environment variables and tools for get-profile retrieval.
  • An AI application, such as Claude Desktop, utilizes the get-profile tool to fetch comprehensive profiles of decision-makers at various companies in the tech sector.

🛠️ Advanced Configuration & Security

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

Configuration Sample:

{
  "mcpServers": {
    "[server-name]": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-[name]"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

Security Considerations:

• OAuth 2.0: Securely manage authentication and token refreshes.
• Data Privacy: Use minimal data storage with selective field return to protect user privacy.

FAQs

1. What MCP clients are compatible with LinkedInMCP?

   • The server is fully compatible with Claude Desktop, Continue, and Cursor for resources and tools.

2. How can I set up the MCP server for integration with an AI application?

   • Set up environment variables in a .env file and configure the claude_desktop_config.json.

3. Can I use LinkedInMCP to retrieve detailed profile information?

   • Yes, by utilizing the provided tools such as get-profile, you can fetch comprehensive profiles of specific users.

4. What is the process for setting up the MCP server?

   • Follow the steps in the README: clone the repository, install dependencies, and run the server using npm scripts.

5. How does LinkedInMCP ensure data privacy during interactions with LinkedIn's API?

   • Minimal data storage and selective field return are used to protect user privacy while ensuring secure and efficient interactions.

Conclusion

LinkedInMCP revolutionizes AI application integration by providing a standardized MCP protocol for interfacing with the powerful LinkedIn API. By leveraging advanced search capabilities, detailed profile retrieval, and comprehensive job market intelligence, this server enhances the functionality of AI applications such as Claude Desktop and Continue. With its robust security measures and compatibility across various MCP clients, LinkedInMCP is an essential tool for developers looking to build sophisticated AI solutions that interact seamlessly with LinkedIn data.

By integrating LinkedInMCP into your AI workflow, you can leverage the vast professional network on LinkedIn while maintaining privacy and security.