Apify
Extract data from any website with thousands of scrapers, crawlers, and automations on Apify Store. Use ready-made tools to access
This MCP implementation, developed using TypeScript, provides a robust foundation for building and deploying web scraping and automation projects. It leverages the Apify platform and Crawlee library, offering a structured environment for creating scalable web crawlers and data extraction tasks. The implementation includes configuration files for ESLint, TypeScript, and Docker, ensuring code quality and consistency across different development environments. By abstracting common web scraping challenges and providing integration with Apify's cloud infrastructure, this tool enables developers to focus on building complex data acquisition workflows. It is particularly useful for projects requiring large-scale web data extraction, automated testing of web applications, or building AI training datasets from web sources.
Connection
- streamable_http · auth: api_key · free_tier —
https://mcp.apify.com/
Source
Repository: https://github.com/apify/apify-mcp-server
Maintain Apify?
Let people know it's listed here — add the badge (live metrics, light/dark aware) or a plain link to your README or docs.
[](https://getagentictools.com/mcp/apify-apify-mcp-server?ref=badge) Related mcp
DevDocs Free, private, UI-based software documentation context management server. Designed with software developers in mind.
Agent Mail Coordinates multiple agents through structured messaging, exclusive file path claims, and persistent Git-based project archives wi
pg-aiguide Version-aware PostgreSQL documentation and best practices for AI coding assistants
Clerk Manage Clerk's authentication and user management organization management, session handling, and authorization features.
Financial Datasets Provides direct access to stock market data including income statements, balance sheets, cash flow statements, historical prices,
Crawl4AI RAG Integrates web crawling with RAG functionality to enable website content retrieval, storage in vector databases, and semantic sear