LinkedIn Jobs MCP Server

LinkedIn Jobs MCP Server

The LinkedIn Jobs MCP Server bridges the gap between AI assistants and real‑time job market data. By exposing LinkedIn’s extensive career listings through the Model Context Protocol, it lets Claude and other Anthropic models retrieve current openings, filter them by location or keyword, and dive into job details—all without leaving the conversational flow. This eliminates the need for developers to manually query LinkedIn or build custom scrapers, ensuring that AI‑powered applications can offer users up‑to‑date employment information with minimal overhead.

At its core, the server offers three intuitive tools. accepts a keyword string, optional result limit, and a location parameter (defaulting to Israel) and returns a concise list of relevant postings. pulls the full description, responsibilities, and application link for a specific job ID, allowing assistants to present deep dives or share direct URLs. resolves human‑readable location names to LinkedIn’s internal IDs, enabling precise geographic filtering. These tools are wrapped in a lightweight Python service that runs over stdio, making it straightforward to launch from the Claude Desktop client or any other MCP‑compatible host.

Developers benefit from a single, well‑defined interface that abstracts away API keys, pagination, and data transformation. The server handles authentication with RapidAPI behind the scenes, parses responses into JSON objects, and formats output for readability. This means that an assistant can answer questions like “Show me software engineering roles in Berlin” or “What are the latest AI positions in Tel Aviv?” with a single tool call, delivering results that feel native to the chat experience. The ability to fetch detailed job descriptions also opens possibilities for building recommendation engines, résumé‑matching features, or personalized career counseling flows.

Integration is seamless: after configuring the MCP server in , Claude automatically discovers the available tools and can invoke them on demand. Because MCP treats each tool as a callable action, developers can compose complex workflows—such as searching for jobs, filtering by salary range, and then saving favorites—by chaining tool calls within a single conversation. The server’s stateless design ensures that each request is independent, which scales well for high‑volume use cases like recruitment agencies or career portals.

Unique advantages of this MCP server include real‑time data access, built‑in location resolution, and a minimal footprint that requires only Python 3.8+ and a RapidAPI key. Its focus on LinkedIn’s job ecosystem makes it ideal for hiring platforms, career advice bots, or internal talent‑matching tools that need authoritative listings without the overhead of maintaining their own data pipeline. By turning LinkedIn’s vast job database into a first‑class tool for AI assistants, the server empowers developers to create richer, more actionable user experiences in a matter of minutes.