mcp-server

linkedin-mcp-server: Integrate AI Agents

Name: linkedin-mcp-server: Integrate AI Agents
Author: stickerdaniel

Give AI agents access to LinkedIn data with linkedin-mcp-server, suitable for founders integrating AI tools, backed by 2.9k+ GitHub stars.

3,029 stars517 forksPythonHealth Score 9/10Updated 8/6/2026100% free · open source

What it does

The linkedin-mcp-server tool allows you to give AI agents like Claude access to LinkedIn data such as profiles, companies, jobs, and messages through an open-source MCP server.

Install / run

git clone https://github.com/stickerdaniel/linkedin-mcp-server.git

When to use it

•You need to automate tasks on LinkedIn using AI agents.
•You want to integrate LinkedIn data with other tools and services using MCP-compatible AI agents.
•You need to analyze or process large amounts of LinkedIn data using AI-powered tools.

Quick start

1Navigate to the cloned repository with cd linkedin-mcp-server
2Create a new file named config.json and add your LinkedIn API credentials and other configuration settings
3Run the server using python app.py
4Use a tool like curl or a web browser to test the API endpoints, such as GET /profiles
5Integrate the linkedin-mcp-server with an MCP-compatible AI agent like Claude by configuring the agent to use the server's API

Ready-to-paste prompt

curl -X GET 'http://localhost:5000/profiles?query=software+engineer' -H 'Authorization: Bearer YOUR_API_TOKEN'

Heads up: You need to have a LinkedIn API key and configure the config.json file with the correct credentials and settings to use the linkedin-mcp-server tool.

Saves to your device

Topics

ai-agents

anthropic

chatgpt

chatgpt-desktop

claude

claude-ai

claude-code

claude-desktop

desktop-extension

dxt

linkedin-api

linkedin-mcp

linkedin-profile-scraper

linkedin-scraper

mcp

mcp-server

model-context-protocol

python

How linkedin-mcp-server: Integrate AI Agents works

Codeflow

Free to inspect

Linkedin-mcp-server: Integrate AI Agents is a large Python project (~34k lines across 71 code files, plus 57 test files). Setup is light: runs with Docker — add your API keys to .env. Last commit this month, Apache-2.0 license, has a test suite.

Size

Large codebase

~34k lines · 71 code files · ~5 h to skim

Setup

Light setup

Runs with Docker — add your API keys to .env.

Runs on

Python · Docker

Needs API keys (.env)

Python 100%

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