microsoft/playwright-mcp
Playwright MCP is an MCP server that enables LLMs to automate and interact with web browsers using structured accessibility snapshots for efficient and deterministic browser automation.
Awesome MCP › Browser Automation
stickerdaniel/linkedin-mcp-server
The LinkedIn MCP Server is a Model Context Protocol (MCP) server designed to facilitate interaction with LinkedIn through AI assistants such as Claude. It enables users to scrape and retrieve detailed information from LinkedIn profiles, companies, and job postings, as well as perform job searches and get personalized job recommendations. The server operates within a Docker container, making it easy to deploy and run on local machines. Users can connect AI assistants to their LinkedIn accounts by providing a LinkedIn session cookie, allowing seamless access to LinkedIn data for various use cases. Key features of the LinkedIn MCP Server include profile scraping, which extracts comprehensive details from LinkedIn profiles such as work history, education, skills, and connections. It also supports company analysis by retrieving detailed company information from LinkedIn company profiles. Job-related functionalities include fetching specific job posting details, searching for jobs using filters like keywords and location, and obtaining personalized job recommendations based on the user's profile. The server also manages browser sessions efficiently to ensure resource cleanup. The project provides multiple installation methods, with Docker setup being the recommended universal approach. It includes detailed instructions on obtaining the necessary LinkedIn cookie for authentication, either through Chrome DevTools or a Docker command. The server supports different transport modes, including standard input/output and streamable HTTP, to accommodate various client configurations. Additionally, there is a Claude Desktop extension for easy integration with the Claude AI assistant. The LinkedIn MCP Server is actively maintained, with all tools functional as of July 2025. It is designed to enhance AI assistant capabilities by providing direct access to LinkedIn data, enabling tasks such as researching candidates, analyzing companies for partnerships, and improving CVs to target specific job postings. The project emphasizes ease of use, flexibility, and robust functionality for LinkedIn data interaction through the MCP framework.
https://github.com/stickerdaniel/linkedin-mcp-server
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