Apify MCP Server Integration

What does “Apify” MCP Server do?

The Apify MCP (Model Context Protocol) Server acts as a bridge between AI assistants and the Apify platform, allowing AI systems to interact seamlessly with Apify Actors—cloud-based scripts for web automation, data extraction, and workflow automation. By exposing Actors through the MCP protocol, this server lets AI clients trigger, manage, and retrieve results from Actors. This enhances development workflows by enabling tasks such as running web scrapers, automating browser actions, or orchestrating complex data pipelines, all accessible via standardized MCP tools and resources. The server supports both HTTP (SSE) and local stdio modes, making it flexible for integration in various environments.

List of Prompts

No explicit prompt templates are mentioned in the provided repository content.

Ready to grow your business?

Start your free trial today and see results within days.

Request demo Log in

List of Resources

No explicit MCP resources are detailed in the available documentation or file listings.

List of Tools

No detailed list of tools (such as query_database, read_write_file, call_api, or Actor-triggering tools) are described in the files or documentation available via the repository overview. The server enables interaction with Apify Actors, but specific tool names or descriptions are not present.

Join our newsletter

Get latest tips, trends, and deals for free.

Email address

Subscribe

Use Cases of this MCP Server

• Web Data Extraction: Developers can trigger Apify Actors for scraping web pages and extracting structured data, automating repetitive data collection tasks.
• Automation of Workflows: AI clients can orchestrate multi-step automation using Apify Actors, streamlining business processes or research pipelines.
• Browser Automation: The server allows AI assistants to automate browser tasks, such as form filling, navigation, or testing, by leveraging browser automation Actors.
• Integration with External APIs: Through Apify Actors, developers can connect their AI workflows to external APIs and services, facilitating seamless data integration.

How to set it up

Windsurf

1. Ensure you have Node.js installed.
2. Locate your Windsurf configuration file (typically windsurf.config.json).
3. Add the Apify MCP Server with the following JSON snippet:

   {
     "mcpServers": {
       "apify-mcp": {
         "command": "npx",
         "args": ["@apify/actors-mcp-server@latest"]
       }
     }
   }
   

4. Save your configuration and restart Windsurf.
5. Verify that the Apify MCP Server appears in the MCP server list.

Securing API Keys

Example:

{
  "mcpServers": {
    "apify-mcp": {
      "command": "npx",
      "args": ["@apify/actors-mcp-server@latest"],
      "env": {
        "APIFY_TOKEN": "${APIFY_TOKEN}"
      },
      "inputs": {
        "actorId": "your-actor-id"
      }
    }
  }
}

Claude

1. Ensure Node.js is installed.
2. Open the Claude MCP configuration file.
3. Add the Apify MCP Server entry:

   {
     "mcpServers": {
       "apify-mcp": {
         "command": "npx",
         "args": ["@apify/actors-mcp-server@latest"]
       }
     }
   }
   

4. Save and restart Claude.
5. Confirm the server is available.

Securing API Keys

{
  "mcpServers": {
    "apify-mcp": {
      "command": "npx",
      "args": ["@apify/actors-mcp-server@latest"],
      "env": {
        "APIFY_TOKEN": "${APIFY_TOKEN}"
      },
      "inputs": {
        "actorId": "your-actor-id"
      }
    }
  }
}

Cursor

1. Install Node.js.
2. Edit your Cursor MCP configuration file.
3. Add:

   {
     "mcpServers": {
       "apify-mcp": {
         "command": "npx",
         "args": ["@apify/actors-mcp-server@latest"]
       }
     }
   }
   

4. Save and restart Cursor.
5. Check that Apify MCP Server is listed.

Securing API Keys

{
  "mcpServers": {
    "apify-mcp": {
      "command": "npx",
      "args": ["@apify/actors-mcp-server@latest"],
      "env": {
        "APIFY_TOKEN": "${APIFY_TOKEN}"
      },
      "inputs": {
        "actorId": "your-actor-id"
      }
    }
  }
}

Cline

1. Ensure Node.js is installed.
2. Update the Cline MCP server configuration file.
3. Insert:

   {
     "mcpServers": {
       "apify-mcp": {
         "command": "npx",
         "args": ["@apify/actors-mcp-server@latest"]
       }
     }
   }
   

4. Save and restart Cline.
5. Confirm the MCP server is operational.

Securing API Keys

{
  "mcpServers": {
    "apify-mcp": {
      "command": "npx",
      "args": ["@apify/actors-mcp-server@latest"],
      "env": {
        "APIFY_TOKEN": "${APIFY_TOKEN}"
      },
      "inputs": {
        "actorId": "your-actor-id"
      }
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "apify-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change "apify-mcp" to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the available documentation, the Apify MCP Server provides a robust bridge to Apify Actors but lacks detailed documentation of MCP-specific prompts, resources, or tool schemas in the public README and file list. The setup process is well-documented and security best practices are included. As a result, the server is highly practical for Apify users, but less informative for generic MCP integrations.

MCP Score

Our opinion:
Given the lack of explicit MCP prompt, resource, and tool definitions, but the presence of good setup docs and open source licensing, we would rate this MCP server at 5/10 for general MCP use. If your primary need is integrating Apify Actors into AI workflows, it’s very useful; for broader MCP scenarios, more detailed documentation would be helpful.