hiveflow-mcp-server

hiveflow-mcp-serverは、HiveFlowのための公式なモデルコンテキストプロトコル(MCP)サーバーです。このツールを使用することで、AIアシスタント(Claude、Cursorなど)をHiveFlowの自動化プラットフォームに直接接続できます。これにより、フローの管理や実行、APIキーの取得などが簡単に行えるようになります。開発者はこのサーバーを通じて、さまざまな自動化タスクを効率的に実行できるため、AIを活用したプロジェクトに最適です。

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@hiveflow/mcp-server

Official Model Context Protocol (MCP) server for HiveFlow. Connect your AI assistants (Claude, Cursor, etc.) directly to your HiveFlow automation platform.

🚀 Quick Start
Installation
npm install -g @hiveflow/mcp-server
Configuration

Add to your MCP client configuration (e.g., .cursor/mcp.json):

{
  "mcpServers": {
    "hiveflow": {
      "command": "npx",
      "args": ["-y", "@hiveflow/mcp-server"],
      "env": {
        "HIVEFLOW_API_KEY": "your-api-key-here",
        "HIVEFLOW_API_URL": "https://api.hiveflow.ai"
      }
    }
  }
}
For Local Development
{
  "mcpServers": {
    "hiveflow": {
      "command": "npx",
      "args": ["-y", "@hiveflow/mcp-server"],
      "env": {
        "HIVEFLOW_API_KEY": "your-api-key-here",
        "HIVEFLOW_API_URL": "http://localhost:5000"
      }
    }
  }
}
🔑 Getting Your API Key
Option 1: From HiveFlow Dashboard
  1. Log in to your HiveFlow dashboard
  2. Go to Settings > API Keys
  3. Generate a new API key
Option 2: From Command Line (Self-hosted)
cd your-hiveflow-backend
node get-api-key.js your-email@example.com
🛠️ Available Tools

Once configured, you'll have access to these tools in your AI assistant:

Flow Management
  • create_flow - Create new automation flows
  • list_flows - List all your flows
  • get_flow - Get details of a specific flow
  • execute_flow - Execute a flow with optional inputs
  • pause_flow - Pause an active flow
  • resume_flow - Resume a paused flow
  • get_flow_executions - Get execution history
MCP Server Management
  • list_mcp_servers - List configured MCP servers
  • create_mcp_server - Register new MCP servers
📊 Available Resources
  • hiveflow://flows - Access to all your flows data
  • hiveflow://mcp-servers - MCP servers configuration
  • hiveflow://executions - Flow execution history
💡 Usage Examples
Create a New Flow
AI: "Create a flow called 'Email Processor' that analyzes incoming emails"
List Active Flows
AI: "Show me all my active flows"
Execute a Flow
AI: "Execute the flow with ID 'abc123' with input data {email: 'test@example.com'}"
Get Flow Status
AI: "What's the status of my Email Processor flow?"
🔧 Configuration Options
Environment Variables
  • HIVEFLOW_API_KEY - Your HiveFlow API key (required)
  • HIVEFLOW_API_URL - Your HiveFlow instance URL (default: https://api.hiveflow.ai)
  • HIVEFLOW_INSTANCE_ID - Instance ID for multi-tenant setups (optional)
Command Line Options
hiveflow-mcp --api-key YOUR_KEY --api-url https://your-instance.com
🏗️ Architecture

This MCP server acts as a bridge between your AI assistant and HiveFlow:

AI Assistant (Claude/Cursor) ↔ MCP Server ↔ HiveFlow API
🔒 Security
  • API keys are transmitted securely over HTTPS
  • All requests are authenticated and authorized
  • No data is stored locally by the MCP server
🐛 Troubleshooting
Common Issues

"HIVEFLOW_API_KEY is required"

  • Make sure you've set the API key in your MCP configuration
  • Verify the API key is valid and not expired

"Cannot connect to HiveFlow API"

  • Check that your HiveFlow instance is running
  • Verify the API URL is correct
  • Ensure there are no firewall restrictions

"MCP server not found"

  • Restart your AI assistant completely
  • Verify the MCP configuration file is in the correct location
  • Check that the package is installed: npm list -g @hiveflow/mcp-server
Debug Mode

For detailed logging, set the environment variable:

export DEBUG=hiveflow-mcp:*
📚 Documentation
🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

📄 License

MIT License - see LICENSE file for details.

🆘 Support

Made with ❤️ by the HiveFlow team

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