LinkedIn Posts Hunter MCP Server – Overview

The LinkedIn Posts Hunter is a Model Context Protocol (MCP) server designed to give AI assistants the ability to automatically search, scrape, and manage LinkedIn job posts directly from your local environment. By leveraging a Playwright‑based browser automation tool, the server authenticates with LinkedIn, captures session cookies, and performs keyword searches that return real‑time job post data. This eliminates the need for manual browsing and lets developers and recruiters keep pace with opportunities that surface on LinkedIn before they appear on traditional job boards.

The server’s core value lies in its end‑to‑end workflow. Once authenticated, the AI can invoke a tool that scrapes posts matching user‑defined keywords. The scraped data—including author, post content, engagement metrics, and timestamps—is persisted in a local SQLite database. This guarantees that sensitive data never leaves the user’s machine, addressing privacy concerns while still enabling robust analytics. A complementary React dashboard exposes this data visually, allowing users to view posts in table or card formats, filter by engagement or date, and flag entries as “applied” or “saved for later.” The dashboard synchronizes with the MCP tools through polling, ensuring that any changes made via natural‑language commands are immediately reflected in the UI.

Key capabilities of this MCP server include:

• Persistent LinkedIn authentication that stores session cookies locally, eliminating repeated logins.
• Automated job‑post scraping via Playwright, enabling rapid collection of up to thousands of posts per query.
• Local data storage in SQLite, providing a lightweight, secure repository for post metadata and application status.
• Dual interaction model: users can manage posts either through the AI’s natural‑language commands or the intuitive point‑and‑click React dashboard.
• Real‑time synchronization between MCP commands and the UI, achieved through periodic polling of the local database.

Real‑world scenarios that benefit from this server include:

• Recruiters who need to surface niche roles quickly across multiple industries without sifting through LinkedIn manually.
• Job seekers who want an early‑bird advantage by discovering posts that surface on LinkedIn before posting sites update.
• Talent acquisition teams looking to track engagement metrics of posts and monitor application pipelines in a single, secure location.

Integration into existing AI workflows is straightforward: any MCP‑compatible client (Claude Desktop, Cursor, or custom assistants) can call the exposed tools (, , , etc.) and receive structured JSON responses. The AI can then incorporate search results into its conversational context, suggest follow‑up actions, or trigger the dashboard for visual inspection. By combining natural language interaction with a powerful UI, the LinkedIn Posts Hunter MCP Server offers developers and recruiters a comprehensive, privacy‑preserving solution for LinkedIn job post automation.