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AS

apify/apify-mcp-server

AI Agent

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.

3.6k Stars222 Forks127 Open Issues3.6k WatchersTypeScriptMIT
AI AgentMCP ServerAutomationData ToolInfrastructure
Review Readiness

This repository page has enough original analysis, source evidence, and comparison context to be treated as an indexable Git-Stars review.

100

review score

Indexable review
Decision Snapshot

Problem solved

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.

Deployment reality

The available setup signal starts with: npx @apify/actors-mcp-server. Treat this as a starting point, then ask Codex or Claude to inspect the README, environment variables, runtime version, and deployment target before production use.

Commercial use

The recorded license is MIT. This is a useful commercial-use signal, but teams should still verify license text, dependencies, model/API terms, and trademark constraints.

Capability ceiling

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.

Source and compliance noteLast synced: Aug 14, 2026

Git-Stars is independent and not affiliated with GitHub or this project. Analysis may be AI-assisted and based on public repository metadata plus short README-derived summaries. We do not mirror full README files, docs, issues, or social comments.

Original GitHub sourceMethodologyEditorial Policy
Editorial Evaluation

apify/apify-mcp-server is tracked as a TypeScript project in the AI Agent, MCP Server, Automation, Data Tool, Infrastructure area. This evaluation combines public GitHub metadata, category signals, short source summaries, and Git-Stars editorial rules rather than copying project documentation.

Momentum check: the repository has 3.6k total stars, with +136 today, +0 this week, and +0 this month. These growth windows help distinguish durable adoption from short-lived visibility spikes.

Maintenance check: current activity is Active; the latest push was 10 days ago, and the open issue queue is 127, about 3.58% of total stars. Treat this as an adoption signal, not a substitute for engineering due diligence.

Adoption check: 222 forks and 3.6k watchers suggest how often the project is reused or followed. License signal: MIT. Always verify license compatibility before commercial or internal use.

Practical fit: this project is most relevant when you need AI prototypes, LLM workflows, and agent-style applications. Be cautious when you have low tolerance for large unresolved issue queues.

Source check: Git-Stars currently has 2 explicit source reference(s) for this report and a recent growth signal of 136. Follow the original GitHub repository for final install, security, and release information.

Evidence Checklist

Analysis

Ready

Original problem, capability, and verdict guidance are available.

Sources

Ready

Repository metadata and README/source references are attached.

License

Ready

MIT is recorded for review.

Maintenance

Ready

Recent activity is visible in repository metadata.

Alternatives

Ready

Enough nearby projects exist for comparison.

Best For
  • AI prototypes, LLM workflows, and agent-style applications
  • TypeScript teams evaluating ecosystem-native tooling
  • use cases where recent maintenance matters
Avoid When
  • you have low tolerance for large unresolved issue queues
  • you need a legal review, security audit, or production SLA
Adoption Signals

Momentum

3.6k Stars

Reuse

222 Forks

Attention

3.6k Watchers

Maintenance

active

License

MIT

Open issues

127

Overview

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.

Key Features

- 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

Tool Positioning

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

Quick Start
npx @apify/actors-mcp-server
View on GitHub Project Homepage
Project Activity

73

Health Score

Active

Commit Activity

Jan 2, 2025

Created

Aug 13, 2026

Last push

Source Trail

GitHub repository metadata

metadata

GitHub README

readme_summary

Star History

+0

Today's growth

+0

7-day growth

+0

30-day growth

Aug 14, 2026Aug 14, 2026
Community Health
222

Forks

127

Open

3.6k

Watchers

Owner
AS

apify

GitHub profile
Topics & Language
TypeScriptagentsaimcpmcp-server
Ecosystem & Usage
GitHub Repository Project Website Search on npm
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License
MIT
CreatedJan 2, 2025
Last pushAug 13, 2026
Last syncedAug 14, 2026
Repository Standards

✓

License

✓

Forked

✓ Active

Maintained

AI AnalysisAnalyzed by Git-Stars

Problem Solved

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.

Capabilities

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.

Bottom Line

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.

Full AI Analysis