Apify Actors MCP Server Detailed Introduction

Project Overview

The Apify Actors MCP Server is a server implementation based on the Model Context Protocol (MCP), specifically designed to provide a unified interface for accessing all Actors on the Apify platform. This project enables AI applications and AI agents to interact with Apify's data extraction tools through a standardized protocol.

Project Address: https://github.com/apify/actors-mcp-server Main Use: Integrating over 200 data extraction tools from the Apify Store as AI tools into various AI applications Supported Platforms: Claude Desktop, Visual Studio Code, LibreChat, and other MCP clients

Core Features

🔧 Dual Deployment Modes

• MCP Server Actor: Runs as an HTTP server on the Apify platform, providing access via Server-Sent Events (SSE)
• Local Stdio Server: Runs on a local machine, communicating with the client via standard input/output (stdio)

🛠️ Rich Tool Integration

The project comes pre-configured with the following core Actor tools:

• Instagram Scraper (apify/instagram-scraper): Extracts Instagram posts, profiles, locations, photos, and comments
• RAG Web Browser (apify/rag-web-browser): Performs web searches, crawls page content, and returns structured data
• Google Maps Contact Details Extractor (lukaskrivka/google-maps-with-contact-details): Extracts business contact information from Google Maps

🔍 Intelligent Tool Discovery

• get-actor-details: Retrieves documentation, input schema, and details for a specific Actor
• discover-actors: Searches for relevant Actors using keywords and returns detailed information
• Dynamic Tool Management: Supports adding and removing Actor tools at runtime (requires client support)

🎯 Multi-Client Support

Supports the following MCP clients:

• Claude Desktop: Via Stdio transport protocol
• Visual Studio Code: Supports Stdio and SSE transport
• LibreChat: Supports Stdio and SSE transport
• Apify Test Client: Full SSE support and authorization headers
• Other standard MCP clients

Technical Architecture

Protocol Support

• MCP (Model Context Protocol): Open protocol for secure and controllable interaction between AI applications and external tools
• Transport Methods:
  • Server-Sent Events (SSE) - For remote HTTP servers
  • Standard Input/Output (Stdio) - For local process communication

Input Processing Optimization

• Description Truncation: Limits descriptions to 500 characters for improved compatibility
• Enum Field Optimization: Merges enums with length limits within 200 characters
• Required Field Marking: Clearly identifies required parameters
• Nested Property Construction: Handles complex structures like proxy configurations
• Type Inference: Automatically infers array item types

Usage Scenario Examples

Data Research and Analysis

"Search the web and summarize the latest trends in AI agents"
"Find and analyze The Rock's Instagram profile"

Business Information Gathering

"Find the top 10 best Italian restaurants in San Francisco"
"Extract Google Maps business contact information for a specific area"

Content Creation Support

"Provide a step-by-step guide on using the Model Context Protocol and source URLs"
"Analyze the latest post trends on a Facebook page"

Installation and Configuration

Claude Desktop Configuration

{
  "mcpServers": {
    "actors-mcp-server": {
      "command": "npx",
      "args": ["-y", "@apify/actors-mcp-server"],
      "env": {
        "APIFY_TOKEN": "your-apify-token"
      }
    }
  }
}

Visual Studio Code Configuration

{
  "mcp": {
    "inputs": [
      {
        "type": "promptString",
        "id": "apify_token",
        "description": "Apify API Token",
        "password": true
      }
    ],
    "servers": {
      "actors-mcp-server": {
        "command": "npx",
        "args": ["-y", "@apify/actors-mcp-server"],
        "env": {
          "APIFY_TOKEN": "${input:apify_token}"
        }
      }
    }
  }
}

Custom Actor Configuration

{
  "command": "npx",
  "args": [
    "-y", "@apify/actors-mcp-server",
    "--actors", "lukaskrivka/google-maps-with-contact-details,apify/instagram-scraper"
  ],
  "env": {
    "APIFY_TOKEN": "your-apify-token"
  }
}

System Requirements

Basic Environment

• Node.js: v18 or higher
• Operating System: macOS, Windows, or Linux
• Apify API Token: Required to access Apify platform services

Client Requirements

• Latest version of Claude Desktop
• Visual Studio Code (with MCP extension support)
• Or other compatible MCP client

Performance Limitations

Resource Configuration

• Memory Limit: 4GB memory limit per Actor
• Free Users: Total 8GB limit, with 128MB reserved for the MCP server
• Concurrent Processing: Supports multiple Actors running simultaneously

Architecture Optimization

• Schema Handling: JSON Schema standard compliant
• Description Optimization: Automatic truncation and formatting
• Error Handling: Comprehensive exception handling mechanism

Development and Debugging

MCP Inspector Tool

export APIFY_TOKEN=your-apify-token
npx @modelcontextprotocol/inspector npx -y @apify/actors-mcp-server

Example Clients

The project provides complete example code:

• Stdio Client: examples/clientStdio.ts
• SSE Client: examples/clientSse.ts

Project Summary

The Apify Actors MCP Server represents a significant advancement in AI application tool integration, bringing powerful data extraction capabilities to various AI applications through the standardized MCP protocol. The project's main value includes:

Core Advantages

1. Unified Interface: Access to over 200 specialized data extraction tools through a single protocol
2. Flexible Deployment: Supports both cloud and local deployment modes
3. Broad Compatibility: Integrates with mainstream AI development tools and platforms
4. Easy to Extend: Supports dynamic addition and customization of Actor tools

Application Value

• Enhance AI Capabilities: Provides AI applications with real-time data acquisition capabilities
• Simplify Integration: Standardized interface reduces development complexity
• Expand Possibilities: Open architecture supports unlimited expansion
• Enterprise Ready: Mature platform and comprehensive documentation support

Suitable Scenarios

• AI Agent Development
• Data Analysis and Research
• Content Creation and Management
• Business Intelligence and Market Research
• Automated Data Collection

This project provides a powerful, flexible, and easy-to-use solution for the data acquisition needs of AI applications, making it an important component of the modern AI tool stack.