LinkedIn MCP Server

Overview

The LinkedIn MCP Server is an open-source Model Context Protocol (MCP) server that bridges AI agents (such as Claude, Cursor, and other MCP-compatible tools) with LinkedIn. It allows large language models to interact directly with your professional network using natural language, without custom API integrations.

Built primarily with Python and Selenium (with variants using official APIs or cookies), it keeps your credentials local and runs securely on your device or in Docker. Multiple community implementations exist, including TypeScript versions for API-based access.

Key Features

• Profile Access & Analysis: Fetch and analyze detailed LinkedIn profiles, including experience, skills, and connections.
• Company Information: Retrieve in-depth company pages and data.
• Job Search & Recommendations: Perform natural language job searches, get personalized recommendations, and view job details.
• Messaging & Networking: Read and manage messages (in supported implementations).
• Secure Authentication: Supports cookie-based auth, OAuth (in API variants), and keeps credentials on-device.
• MCP Compatibility: Works natively with Claude Desktop, Cursor, and other MCP clients via standardized tool calling.
• Deployment Options: Local run, Docker container support, and easy setup for most implementations.

Use Cases

• AI-Powered Job Hunting: Ask your AI to "find software engineering roles in Tokyo matching my experience" and get real-time results with analysis.
• Profile Research: Have Claude summarize a prospect's background before outreach.
• Network Management: Analyze your connections or draft personalized messages.
• Content & Engagement: Generate and post LinkedIn updates (in advanced forks).
• Recruiting Automation: Screen candidates by pulling public profile data into your AI workflow.

How It Works

1. Run the MCP server locally (or in Docker).
2. Connect your MCP-compatible AI client (e.g., Claude Desktop) to the server URL.
3. The AI discovers available LinkedIn tools automatically.
4. Interact via natural language—the agent calls the appropriate MCP tools behind the scenes.

Note: Scraping-based versions use Selenium for browser automation and may be subject to LinkedIn's terms of service. API-based versions use official or unofficial endpoints. Always use responsibly and respect platform policies.

Installation (Popular Python/Selenium Variant)

git clone https://github.com/stickerdaniel/linkedin-mcp-server.git
cd linkedin-mcp-server
# Follow repo-specific setup for dependencies and authentication

Other notable forks:

• TypeScript/API-focused: https://github.com/felipfr/linkedin-mcpserver
• Additional community variants for feeds, jobs, or advanced automation.

Related Resources

• Explore more MCP servers and tools in the growing ecosystem.
• Combine with other MCP servers (e.g., browser, email, or calendar) for powerful multi-tool AI agents.

This server exemplifies how MCP standardizes tool integration, turning LinkedIn into a first-class data source for agentic AI workflows.