book-crawler-mcp
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@book-crawler-mcp auto crawl arXiv cs.AI papers"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Book Crawler MCP
一个自动化的 PDF 书籍爬取和 Gumroad 发布工具,使用 AI 生成商品描述。
功能
工具 | 描述 |
| 从 arXiv 获取学术论文 |
| 从 GitHub 获取热门仓库 |
| 下载 PDF 文件 |
| 提取 PDF 内容和元数据 |
| 使用 AI 生成 Gumroad 商品描述 |
| 发布商品到 Gumroad |
| 一键全自动爬取并发布 |
| 查看已发布的商品 |
Related MCP server: Fetcher MCP
安装
1. 克隆并安装依赖
git clone https://github.com/LiZhuBin/book-crawler-mcp.git
cd book-crawler-mcp
npm install
npm run build2. 配置环境变量
复制 .env.example 为 .env 并填入你的密钥:
cp .env.example .env# Gumroad API Token
# 获取方式:https://gumroad.com/settings -> Advanced
GUMROAD_ACCESS_TOKEN=your_token_here
# Anthropic API Key (用于 AI 生成商品描述)
# 获取方式:https://console.anthropic.com/settings/keys
ANTHROPIC_API_KEY=your_key_here使用
MCP 配置
在 Claude 的 MCP 配置文件中添加:
{
"mcpServers": {
"book-crawler": {
"command": "npx",
"args": ["book-crawler-mcp"],
"env": {
"GUMROAD_ACCESS_TOKEN": "your_token",
"ANTHROPIC_API_KEY": "your_key"
}
}
}
}使用示例
1. 从 arXiv 获取论文
{
"categories": ["cs.AI", "cs.LG", "cs.CL"],
"limit": 10
}2. 下载并提取 PDF 内容
{
"url": "https://arxiv.org/pdf/2301.00001.pdf"
}3. 生成 Gumroad 商品描述
{
"pdfContent": "提取的 PDF 内容...",
"title": "论文标题",
"author": "作者名"
}4. 发布到 Gumroad
{
"name": "商品名称",
"description": "商品描述 (Markdown)",
"price": 1999,
"currency": "USD"
}5. 全自动模式
{
"source": "arxiv",
"categories": ["cs.AI", "cs.LG"],
"autoPublish": true,
"priceRange": {
"min": 999,
"max": 4999
}
}工作流程
┌─────────────────────────────────────────────────────────────┐
│ 1. fetch_arxiv_papers / fetch_github_repos │
│ → 获取热门资源列表 │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ 2. download_pdf │
│ → 下载 PDF 文件到本地 │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ 3. extract_pdf_content │
│ → 提取文本内容和元数据 │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ 4. generate_gumroad_listing │
│ → AI 生成商品标题、描述、定价 │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ 5. publish_to_gumroad │
│ → 上传文件并发布商品 │
└─────────────────────────────────────────────────────────────┘开发
# 安装依赖
npm install
# 开发模式
npm run dev
# 构建
npm run build
# 运行
npm start注意事项
版权: 确保你有权分发爬取的内容
API 限制: 遵守各平台的 API 调用频率限制
定价责任: AI 生成的价格仅供参考,请自行判断
License
MIT
Links
Available Tools
1 toolfetch_github_reposB
Fetch trending or popular repositories from GitHub
Inputs: limit, query, language, minStars.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 1 tool
With only one tool, there is no possibility of overlap or confusion between tools. The agent will always select the correct tool.
A single tool follows a clear verb_noun pattern (fetch_github_repos), and there are no other tools to introduce inconsistency.
The server has only one tool, which feels insufficient and misaligned with the server name 'book-crawler-mcp'. The tool appears unrelated to book crawling, suggesting poor scoping.
The single tool covers only fetching GitHub repos, leaving any book-crawling domain completely uncovered. The surface is severely incomplete.
Maintenance
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