What are the main use cases for LinkedIn MCP?

The main use cases include: 1) Sales automation for finding and reaching out to leads, 2) Recruitment assistance for searching and reviewing candidates, 3) Conversation assistance for managing LinkedIn messages, and 4) Market research for gathering company and industry data.

How do I get started with LinkedIn MCP?

To get started, you need to: 1) Read the installation guide to set up MCP in Claude, Cursor, VS Code, or Windsurf, 2) Review the available tools documentation, and 3) Check usage examples on the official site.

How does authentication work with LinkedIn MCP?

Authentication is handled through LinkedIn cookies. You need to set up a LINKEDIN_COOKIE value in your client configuration. The cookie expires every 30 days and needs to be updated accordingly.

Is LinkedIn MCP safe to use?

According to the sources, LinkedIn MCP operates through a cloud browser safely and automatically, respecting LinkedIn's rate limits while handling automated tasks.

LinkedIn MCP

WebsiteFree TrialAI Customer Service Assistant AI Social Media Assistant

LinkedIn MCP is a Model Context Protocol server that enables AI assistants like Claude to interact with LinkedIn accounts for automated profile analysis, job searching, messaging, and data gathering through a secure cloud browser interface.

Visit Website

Advertise This Tool

https://linkedapi.io/mcp?ref=producthunt

Overview
Alternatives

Product Information

Updated:Aug 28, 2025

What is LinkedIn MCP

LinkedIn MCP (Model Context Protocol) serves as a standardized bridge between AI assistants and LinkedIn's platform, allowing seamless integration of LinkedIn's features with AI capabilities. It's designed to provide AI assistants with controlled access to LinkedIn functionalities, enabling them to perform tasks like profile searching, data analysis, and automated communications while maintaining security and compliance with LinkedIn's policies. The system operates through a cloud browser infrastructure that safely manages LinkedIn interactions without compromising user accounts.

Key Features of LinkedIn MCP

LinkedIn MCP (Model Context Protocol) is a standardized server interface that enables AI assistants like Claude to interact with LinkedIn accounts through secure cloud browsers. It provides automated capabilities for profile management, data extraction, messaging, and analytics while maintaining safety and compliance with LinkedIn's policies.

Cloud Browser Integration: Each LinkedIn account gets a dedicated cloud browser instance, making interactions appear as natural device usage while ensuring security

Standardized API Access: Provides a unified interface for AI assistants to access LinkedIn data and functionality through MCP protocol

Automated Profile Operations: Enables automated profile searching, data extraction, messaging, and engagement while respecting LinkedIn's rate limits

Real-time Data Analysis: Offers tools for gathering and analyzing company data, employee information, and industry trends in real-time

Use Cases of LinkedIn MCP

Sales Automation: Find and qualify leads, analyze prospect profiles, and create personalized outreach campaigns with AI-driven insights

Recruitment: Search for candidates with specific skills, review experience, and automate initial outreach while maintaining personal touch

Market Research: Gather competitive intelligence and industry insights by analyzing company profiles, employee data, and market activities

Conversation Management: AI assistance in reading existing conversations and suggesting contextually appropriate responses for natural communication

Pros

Seamless integration with multiple AI assistants

Secure and compliant automation through dedicated cloud browsers

Comprehensive toolkit for various professional networking tasks

Cons

Requires proper configuration and setup

May have limitations based on LinkedIn's API restrictions

Depends on third-party AI assistants for operation

How to Use LinkedIn MCP

Install MCP Server: Choose and install one of the available LinkedIn MCP servers like linkedapi-mcp, linkedin-mcp-server, or other implementations from GitHub repositories

Configure Authentication: Set up authentication by either providing LinkedIn credentials (email/password) or LinkedIn cookie value in the MCP server configuration file

Set Up MCP Client: Configure an MCP-compatible client like Claude Desktop, VS Code, Cursor etc. by adding the LinkedIn MCP server details to the client's configuration file (usually JSON format)

Configure Environment Variables: Set required environment variables like LINKEDIN_EMAIL, LINKEDIN_PASSWORD, LINKEDIN_COOKIE or LINKEDIN_CLIENT_ID/SECRET depending on the authentication method

Start the MCP Server: Launch the MCP server using the command specified in your configuration (e.g. via npm start, python script, or docker run)

Connect AI Assistant: Connect your AI assistant (like Claude) to the running MCP server to enable LinkedIn interactions through natural language

Test Basic Functions: Try basic functions like profile searches, job searches, or company lookups to verify the setup is working correctly

Enable Advanced Features: Configure additional features like automated messaging, profile analysis, or market research capabilities based on your use case

Monitor Usage: Use the MCP Inspector or logging tools to monitor requests and ensure LinkedIn rate limits and usage policies are being followed

LinkedIn MCP FAQs

LinkedIn MCP (Model Context Protocol) is a server that connects LinkedIn accounts to AI assistants like Claude, Cursor, and VS Code, allowing them to interact with LinkedIn through a cloud browser to perform tasks like profile searching, messaging, and data analysis.

Latest AI Tools Similar to LinkedIn MCP

Heartbeat

Contact for PricingAI Voice Chat Generator AI Customer Service Assistant

Heartbeat is a versatile platform that offers both an online community-building solution and an AI-powered wellness calling system for seniors and at-risk individuals, featuring customizable spaces, real-time communication, and health monitoring capabilities.

Resonoon

FreeAI Customer Service Assistant No-Code & Low-Code

Resonoon is a no-code B2B SaaS platform that enables businesses to create AI-powered voice and chatbots in 30 minutes without technical skills, offering pay-per-conversation pricing.

WhatsBoost

FreemiumAI Customer Service Assistant AI Response Generator

WhatsBoost is an AI-powered WhatsApp marketing platform and official Meta Partner that enables businesses to automate customer communications, manage campaigns, and integrate with multiple services through open APIs.

Vapify

Contact for PricingAI Voice Assistants No-Code & Low-Code AI Customer Service Assistant

Vapify is a white-label platform that enables agencies to offer Vapi.ai's voice AI solutions under their own brand while maintaining control over client relationships and maximizing revenue.

LinkedIn MCP