Redbook Search Comment MCP

Redbook Search Comment MCP – AI‑Powered Automation for 小红书

The Redbook Search Comment MCP server turns the popular Chinese social platform 小红书 (Xiaohongshu) into a programmable resource that AI assistants can interrogate, analyze and interact with. By exposing a set of high‑level tools—login, keyword search, content extraction, comment retrieval and AI‑generated posting—the server eliminates the need for manual browser sessions or custom scrapers. Developers can therefore focus on higher‑level conversational logic while the MCP handles all the intricacies of authentication, navigation and data parsing.

At its core, the server automates a typical user workflow: it logs into 小红书 via QR‑code scanning (saving the session for future runs), searches notes by keyword, fetches complete note details or comments from a given URL, and posts context‑aware replies. The posting tool is particularly valuable; it accepts a comment type (lead‑generation, simple like, question, or professional) and delegates the generation of natural language text to the connected AI client. This tight coupling between data retrieval and generative models allows assistants such as Claude to produce comments that feel organic, tailored to the note’s tone and audience.

Key capabilities include:

• Persistent login – a one‑time QR scan followed by session persistence removes repetitive authentication steps.
• Flexible search – users can request any number of results, and the server returns structured metadata (title, author, timestamp).
• Content extraction – a single URL call yields the full body of a note, making it easy to feed downstream NLP pipelines.
• Comment harvesting – retrieving comment threads enables sentiment analysis, trend spotting or competitive research.
• Smart posting – by specifying a comment type, the AI client can generate persuasive, brand‑aligned replies that drive engagement or lead capture.

Typical use cases span marketing automation, influencer outreach and market research. A brand could instruct an AI assistant to “find the top 10 travel notes, analyze their comment sentiment, and reply with a professional tip.” A researcher might ask the assistant to “collect all comments on a specific note and summarize common pain points.” Because the MCP server runs locally and communicates over standard JSON, it integrates seamlessly into existing AI workflows—whether via Claude for Desktop, a custom LLM orchestration platform, or any MCP‑compatible client.

The standout advantage of this server lies in its modular design: login, analysis, comment generation and posting are isolated yet composable. This makes it straightforward to extend or replace individual components, such as swapping Playwright for another browser engine or adding additional comment templates. The result is a robust, developer‑friendly bridge between AI assistants and the dynamic content ecosystem of 小红书.