xhs-comment-analyzer
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| run_search_taskA | 搜索小红书笔记并批量抓取产品评论，返回分析结果和文件路径。 自动化流程: 搜索匹配笔记 → 逐篇抓取评论 → 关键词提取 + 情感分类 + 热度评分 → 导出 Excel + JSON Args: brand_keywords: 品牌词列表 (必须匹配), 如 ["小熊"] category_keywords: 品类词列表 (必须匹配), 如 ["小家电"] product_keywords: 具体产品词列表 (可选), 如 ["酸奶机", "蒸蛋器"] max_notes: 最多抓取笔记数 (1-200), 默认20 max_comments_per_note: 每篇最多评论数 (1-1000), 默认100 sort_by: 排序方式, general=综合, popularity=热门, time=最新 min_likes: 笔记最低点赞门槛, 默认0 comment_filter: 是否过滤产品相关评论, 默认True Returns: JSON 格式的分析结果摘要，包含统计信息、关键词、情感分布和导出文件路径 |
| scrape_single_noteA | 抓取单篇小红书笔记的评论并分析。 适用于已知笔记URL、只需抓取单篇评论的场景。 Args: note_url: 小红书笔记URL, 如 https://www.xiaohongshu.com/explore/xxxxx max_comments: 最多抓取评论数, 默认100 Returns: JSON 格式的评论列表和分析摘要 |
| analyze_commentsA | 对已抓取的评论JSON文件重新进行关键词/情感/热度分析。 适用于需要用不同参数重新分析已有数据的场景。 Args: file_path: 已抓取的评论JSON文件路径 Returns: JSON 格式的分析结果 |
| check_login_statusA | 检查小红书登录状态，返回是否需要人工登录。 如果未登录，会自动打开浏览器窗口供用户手动登录。 Returns: JSON 格式的登录状态信息 |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
Each tool targets a distinct phase of the workflow: run_search_task for full pipeline, scrape_single_note for single note scraping, analyze_comments for re-analysis, check_login_status for authentication. No overlap or ambiguity.
All tool names follow a consistent verb_noun pattern: run_search_task, scrape_single_note, analyze_comments, check_login_status. The verbs clearly indicate the action and nouns specify the target, making the naming predictable and clear.
With 4 tools, the set is tightly scoped to the server's purpose of comment analysis. Each tool serves a necessary and distinct role without excess. The count is appropriate for the domain.
The set covers the full workflow: login check, scraping (batch and single), and analysis. A minor gap is the lack of a dedicated tool for exporting or managing results beyond what the pipeline returns, but agents can work around this via file paths.