This MCP server uses the Fresh LinkedIn Profile Data API to fetch LinkedIn profile information. It is implemented as a model context protocol (MCP)
This MCP server uses the Fresh LinkedIn Profile Data API to fetch LinkedIn profile information. It is implemented as a model context protocol (MCP) server and
httpx, python-dotenv, and other dependencies. - RAPIDAPI_KEY: Obtain an API key from RapidAPI and add it to a .env file in your project directory (or set it in your environment).Compare MCP servers by category, or browse hosted MCP servers if you prefer an endpoint over a local process.
{
"mcpServers": {
"linkedin_profile_scraper": {
"command": "C:/Users/aiany/.local/bin/uv",
"args": [
"--directory",
"C:/Users/aiany/OneDrive/Desktop/YT Video/linkedin-mcp/project",
"run",
"linkedin.py"
]
}
}
}From the project documentation. Replace placeholders and example paths before connecting.
httpx, python-dotenv, and other dependencies. - RAPIDAPI_KEY: Obtain an API key from RapidAPI and add it to a .env file in your project directory (or set it in your environment).Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
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