Lapras MCP Server

The Lapras MCP Server is a specialized bridge that connects AI assistants—such as Claude or Gemini—to the Lapras job‑matching platform. It addresses a common pain point for developers building career‑advisory or recruitment tools: the difficulty of pulling structured employment data from a third‑party service while keeping user privacy intact. By exposing Lapras’ APIs as MCP tools, the server lets an AI model query job listings, fetch detailed postings, and manage a user’s own career profile—all through declarative tool calls rather than raw HTTP requests.

At its core, the server offers a suite of intuitive tools: search_job for filtering listings by keyword, location or salary; get_job_detail to drill down into a specific posting; and a full CRUD interface for user experiences (get_experiences, create_experience, update_experience, delete_experience). These actions are wrapped in the MCP protocol, so a conversational agent can simply ask for “a remote Rust backend role above ¥8 M” and receive a Markdown table of results, or request the assistant to update their own resume stored in Lapras. The server also handles authentication via a single , ensuring that only authorized users can read or modify their data.

Developers benefit from the server’s tight integration with popular AI workflows. In Claude Desktop, a single DXT file installs the MCP server; in Gemini CLI it can be added to . The server runs natively with Node.js or as a Docker container, giving teams flexibility across environments. Because the MCP interface abstracts away network details, developers can focus on crafting prompts and user experiences instead of managing REST endpoints or SDKs.

Real‑world use cases include building a career coaching chatbot that auto‑generates personalized job recommendations, or an internal talent management tool that syncs employee histories with external job boards. The server’s ability to fetch and update a user’s experience directly from Lapras means the assistant can keep the profile current, ask clarifying questions about past roles, and even suggest résumé improvements—all while respecting data‑privacy policies highlighted in the documentation.

In short, Lapras MCP Server turns a complex, authenticated API into a set of conversational tools that fit naturally into AI‑driven workflows. It empowers developers to create rich, privacy‑aware career services with minimal boilerplate and maximal flexibility.