Xiaohongshu MCP Agent

Overview

The Xiaohongshu API MCP Server is a lightweight micro‑service that exposes the popular Chinese social platform Xiaohongshu (Little Red Book) through a clean, RESTful interface. By wrapping the platform’s underlying web‑scraping and API calls into a single, well‑documented service, the server removes the need for developers to write custom parsers or manage authentication tokens manually. Instead, a client can create a session by submitting a cookie and then perform all common actions—retrieving notes, searching content or users, accessing user profiles, and pulling recommendation feeds—through straightforward HTTP endpoints.

This solution addresses a core pain point for AI assistants: integrating with third‑party social media data without compromising security or stability. Traditional approaches require each assistant to handle cookie rotation, rate limits, and session persistence. The MCP server centralizes these concerns: a single instance can serve multiple AI agents, each with its own isolated client state. The server’s endpoint lets an assistant spin up a new Xiaohongshu session on demand, while subsequent calls are scoped to that client via the path parameter. This isolation ensures that agents do not interfere with one another and can maintain independent authentication contexts.

Key capabilities include:

• Comprehensive content access: fetch notes by ID, scrape HTML‑based notes, and retrieve a user’s entire note collection.
• Search functionality: keyword searches for both notes and users, enabling agents to surface relevant content quickly.
• Feed integration: retrieve recommended feed categories and specific feed types, allowing assistants to mimic the platform’s native discovery experience.
• Health monitoring: a endpoint guarantees that the service is running and ready to accept requests.
• Docker‑ready deployment: a single Docker Compose file brings the server up in minutes, making it trivial to scale horizontally or embed within existing containerized AI pipelines.

In real‑world scenarios, an AI assistant could use this MCP server to answer user queries about trending Xiaohongshu topics, generate content summaries from popular posts, or curate personalized recommendation lists. By delegating the intricacies of session management to the server, developers can focus on higher‑level logic—such as natural language understanding or dialogue flow—while relying on a stable, versioned API surface. The clear separation between client creation and subsequent data retrieval also simplifies testing and debugging, as each agent’s interactions are neatly compartmentalized.

Overall, the Xhs Mcp Agent provides a robust, scalable bridge between AI assistants and Xiaohongshu’s rich content ecosystem. Its RESTful design, coupled with Docker‑based portability, makes it an attractive choice for developers looking to enrich conversational agents with real‑time social media insights without wrestling with low‑level authentication mechanics.