🚀 CrewAI Enterprise MCP Server

🚀 CrewAI Enterprise MCP Server

A Model Context Protocol (MCP) server that provides access to CrewAI Enterprise API for AI agent orchestration and task execution, deployed on the Apify platform.

This Actor enables you to:

• Connect to your CrewAI Enterprise server via MCP
• Start crew tasks with custom inputs
• Monitor crew task status and results
• Monetize your server using Apify's Pay Per Event (PPE) model

✨ Features

• CrewAI Integration: Direct access to CrewAI Enterprise API endpoints
• Built-in charging: Integrated Pay Per Event (PPE) for:
  • Server startup
  • Tool calls (kickoff_crew, get_crew_status)
  • Tool listing
• Easy configuration: Simple setup through Actor input or environment variables
• SSE Transport: Exposes MCP server via Server-Sent Events for real-time communication

🛠 Available Tools

kickoff_crew

Start a new crew task with the provided inputs.

Parameters:

• inputs (object): Dictionary containing the query and other input parameters for the crew

Returns: Dictionary containing the crew task response, including the crew ID needed to check status.

get_crew_status

Get the status of a crew task by its ID.

Parameters:

• crew_id (string): The ID of the crew task to check

Returns: Dictionary containing the crew task status and results.

🚀 Quick Start

1. Configure the Actor

Set up your CrewAI Enterprise server details either through:

Actor Input (Recommended):

• crewaiServerUrl: Your CrewAI Enterprise server URL (e.g., https://your-crewai-server.com/api)
• bearerToken: Bearer token for authenticating with the CrewAI Enterprise API

Environment Variables:

• MCP_CREWAI_ENTERPRISE_SERVER_URL: CrewAI Enterprise server URL
• MCP_CREWAI_ENTERPRISE_BEARER_TOKEN: Bearer token for authentication

2. Deploy and Enable Standby Mode

1. Deploy the Actor to Apify
2. Enable standby mode for the Actor
3. Note the Actor's standby URL

3. Connect Using an MCP Client

Add the following configuration to your MCP client:

{
    "mcpServers": {
        "crewai-enterprise": {
            "url": "https://your-actor.apify.actor/sse"
        }
    }
}

4. Use the Tools

Once connected, you can use the CrewAI tools in your MCP client:

// Start a crew task
const result = await mcpClient.callTool("kickoff_crew", {
    inputs: {
        query: "Analyze market trends for Q1 2024",
        additional_context: "Focus on technology sector"
    }
});

// Check crew status
const status = await mcpClient.callTool("get_crew_status", {
    crew_id: result.crew_id
});

💰 Pricing

This Actor uses the Pay Per Event (PPE) monetization model:

• Server startup: $0.01 per startup
• Tool calls: $0.05 per tool execution (kickoff_crew, get_crew_status)
• Tool listing: $0.001 per list operation

🔧 Configuration

Required Configuration

You must provide either through Actor input or environment variables:

1. CrewAI Server URL: The endpoint URL of your CrewAI Enterprise server
2. Bearer Token: Authentication token for your CrewAI Enterprise API

Optional Configuration

The Actor automatically handles:

• SSE transport setup
• Error handling and retries
• Charging for operations
• Standby mode configuration

📚 Example Usage

Starting a Crew Task

# Example crew inputs
crew_inputs = {
    "task": "Research and analyze competitor pricing strategies",
    "context": {
        "industry": "SaaS",
        "company_size": "startup",
        "target_market": "SMB"
    },
    "agents": ["researcher", "analyst", "writer"]
}

# Start the crew
result = await kickoff_crew(crew_inputs)
crew_id = result["crew_id"]

Monitoring Task Progress

# Check status periodically
status = await get_crew_status(crew_id)

if status["status"] == "completed":
    print("Task completed!")
    print("Results:", status["results"])
elif status["status"] == "running":
    print("Task still running...")
    print("Progress:", status.get("progress", "Unknown"))
else:
    print("Task status:", status["status"])

🔗 Related Resources

• CrewAI Enterprise Documentation
• Model Context Protocol Documentation
• Apify MCP Documentation
• What is Anthropic's Model Context Protocol?

🚀 Deployment

Using Apify CLI

1. Install Apify CLI: npm install -g apify-cli
2. Login: apify login
3. Deploy: apify push

Using Git Integration

1. Connect your Git repository to Apify
2. Push changes to trigger automatic deployment
3. Configure environment variables in Apify Console

🛡️ Security

• Bearer tokens are handled securely through Apify's secret management
• All API communications use HTTPS
• The Actor runs in an isolated container environment
• No sensitive data is logged or stored

📞 Support

For issues related to:

• CrewAI Enterprise: Contact CrewAI support
• Apify Platform: Check Apify documentation or Discord community
• MCP Protocol: See MCP documentation

📄 License

This Actor is provided as-is under standard Apify terms. CrewAI Enterprise is a separate service with its own licensing terms.