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The Apify MCP server enables your AI agents to extract data from social media, search engines, maps, e-commerce sites, or any other website using thousands of ready-made scrapers, crawlers, and automation tools available on the Apify Store.
这个仓库详情页已经具备足够的原创分析、来源证据和同类对比，可以作为 Git-Stars 的可索引评测页。
100
评测分
解决的问题
It solves the problem of giving AI agents real-time, structured web data access without building custom scrapers or dealing with anti-bot measures. It abstracts away the complexity of web scraping, providing a standardized MCP interface to a vast ecosystem of pre-built, maintained scrapers, which would otherwise require significant engineering effort.
部署现实
当前可用的安装信号是：npx @apify/actors-mcp-server。这只能作为起点，正式使用前建议让 Codex 或 Claude 检查 README、环境变量、运行时版本和部署目标。
商业可用性
当前记录的许可证是 MIT。这是重要的商业使用信号，但仍应核验许可证全文、依赖许可证、模型/API 条款和商标限制。
能力上限
Developers can build AI agents that perform tasks like monitoring social media, extracting business leads from maps, aggregating search results, or gathering e-commerce product data. The ceiling is high: with access to thousands of Actors, agents can automate virtually any web data extraction task, including complex multi-step workflows, and can even handle payments for premium Actors. Real-world use cases include competitive intelligence, lead generation, market research, and content aggregation.
Git-Stars 是独立产品，不隶属于 GitHub 或该项目。 分析可能由 AI 辅助生成，依据公开仓库元数据和 README 的短摘要。 我们不镜像完整 README、文档、Issue 或社媒评论。
apify/apify-mcp-server 被追踪为 TypeScript 项目，主要属于 AI Agent, MCP Server, Automation, Data Tool, Infrastructure 方向。这个评估结合公开 GitHub 元数据、分类信号、短来源摘要和 Git-Stars 编辑规则，而不是复制项目文档。
增长检查：该仓库目前有 3.6k Star，今日 +136，本周 +0，本月 +0。这些窗口用于区分持续采用信号和短期曝光峰值。
维护检查：当前活跃度为 一般；最近一次推送距今 62 天，未关闭 Issue 为 127，约占总 Star 的 3.58%。这只是采用信号，不替代工程尽调。
采用检查：222 Fork 和 3.6k Watcher 反映项目被复用和关注的程度。许可证信号：MIT。商业或内部使用前请核验许可证兼容性。
适用判断：当你需要「AI 原型、LLM 工作流和 Agent 类应用」时，这个项目更值得评估；如果「无法接受较大的未解决 Issue 队列」，则需要谨慎。
来源检查：Git-Stars 当前为这份报告保留了 2 个明确来源引用，近期增长信号为 136。最终安装、安全和版本信息仍应以原始 GitHub 仓库为准。
原创分析
充分已经具备问题、能力和结论层面的原创判断。
来源
充分已附带仓库元数据和 README/来源引用。
许可证
充分已记录 MIT，可作为评估依据。
维护
充分仓库元数据中可以看到近期活跃信号。
同类对比
充分已有足够相近项目用于对比。
热度
3.6k 星标
复用
222 复刻
关注
3.6k 关注者
维护
moderate
许可证
MIT
未解决 Issue
127
The Apify Model Context Protocol (MCP) server enables AI agents to extract data from social media, search engines, maps, e-commerce sites, and any other website using thousands of ready-made scrapers, crawlers, and automation tools from the Apify Store. It supports OAuth for easy connection from clients like Claude.ai or Visual Studio Code, and offers both a hosted HTTPS endpoint and a local stdio option.
核心特性
- Access to thousands of ready-made scrapers and automation tools from Apify Store - Supports OAuth for easy connection from popular MCP clients like Claude.ai and VS Code - Offers both hosted HTTPS endpoint and local stdio deployment options
AI Agent
Agent frameworks, autonomous workflows, and tool-use systems
MCP Server
Model Context Protocol servers, clients, and integrations
Automation
Workflow automation, integration glue, and orchestration
Data Tool
Databases, data pipelines, ETL, analytics, and vector search
Infrastructure
Cloud, deployment, networking, containers, and platform tooling
npx @apify/actors-mcp-server63
健康评分
一般
提交活跃度
Jan 2, 2025
创建于
Aug 13, 2026
最近提交
每日同步积累数据后，Star 历史将在此显示。
复刻
未解决
关注者
apify
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✓
许可证
✓
Fork 情况
~ 一般
维护情况
解决的问题
It solves the problem of giving AI agents real-time, structured web data access without building custom scrapers or dealing with anti-bot measures. It abstracts away the complexity of web scraping, providing a standardized MCP interface to a vast ecosystem of pre-built, maintained scrapers, which would otherwise require significant engineering effort.
能力范围
Developers can build AI agents that perform tasks like monitoring social media, extracting business leads from maps, aggregating search results, or gathering e-commerce product data. The ceiling is high: with access to thousands of Actors, agents can automate virtually any web data extraction task, including complex multi-step workflows, and can even handle payments for premium Actors. Real-world use cases include competitive intelligence, lead generation, market research, and content aggregation.
结论
This framework is ideal for developers and organizations that need to integrate web data extraction into AI agents quickly and at scale, without building scraping infrastructure. It should be avoided by those who require fully custom, niche scraping solutions or who have strict data compliance concerns that a third-party service cannot address. The key trade-off is convenience and breadth of tools versus reliance on a commercial platform and potential ethical/legal risks of automated web scraping.