
HiveFlow MCP Server
Connects AI assistants (Claude, Cursor, etc.) directly to the HiveFlow automation platform, allowing them to create, manage, and execute automation flows through natural language commands.
Tools
create_flow
Crea un nuevo flujo de trabajo en HiveFlow
list_flows
Lista todos los flujos de trabajo del usuario
get_flow
Obtiene detalles de un flujo específico
execute_flow
Ejecuta un flujo de trabajo específico
pause_flow
Pausa un flujo activo
resume_flow
Reanuda un flujo pausado
list_mcp_servers
Lista los servidores MCP configurados en HiveFlow
create_mcp_server
Registra un nuevo servidor MCP en HiveFlow
get_flow_executions
Obtiene el historial de ejecuciones de un flujo
README
@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
- Log in to your HiveFlow dashboard
- Go to Settings > API Keys
- 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 flowslist_flows
- List all your flowsget_flow
- Get details of a specific flowexecute_flow
- Execute a flow with optional inputspause_flow
- Pause an active flowresume_flow
- Resume a paused flowget_flow_executions
- Get execution history
MCP Server Management
list_mcp_servers
- List configured MCP serverscreate_mcp_server
- Register new MCP servers
📊 Available Resources
hiveflow://flows
- Access to all your flows datahiveflow://mcp-servers
- MCP servers configurationhiveflow://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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