Hiveflow MCP Server

What is Hiveflow MCP Server about?

Provides a Model Context Protocol server that acts as a bridge between AI assistants and the HiveFlow automation platform, exposing flow‑management tools and resources to the assistant.

How to use Hiveflow MCP Server?

1. Install globally with npm:

   npm install -g @hiveflow/mcp-server
   

2. Add an entry 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"
         }
       }
     }
   }
   

3. Run your AI assistant; it will communicate with the MCP server to invoke HiveFlow operations.

Key features of Hiveflow MCP Server

• Flow management tools: create_flow, list_flows, get_flow, execute_flow, pause_flow, resume_flow, get_flow_executions.
• MCP server management: list_mcp_servers, create_mcp_server.
• Direct resource access via URIs such as hiveflow://flows, hiveflow://executions.
• Secure communication using HTTPS and API keys.
• No local data storage; acts purely as a proxy.
• Debug mode via DEBUG=hiveflow-mcp:*.

Use cases of Hiveflow MCP Server

• Automating repetitive tasks by instructing an AI assistant to create or trigger flows.
• Real‑time monitoring and control of automation workflows through natural language.
• Integrating custom AI agents into existing HiveFlow pipelines without writing additional code.
• Rapid prototyping of new automation scenarios during development.

FAQ from the Hiveflow MCP Server

Q: "HIVEFLOW_API_KEY is required" error? A: Ensure the environment variable is set in the MCP configuration and the key is valid.

Q: Cannot connect to HiveFlow API? A: Verify HIVEFLOW_API_URL points to a running HiveFlow instance and there are no network/firewall restrictions.

Q: MCP server not found by the AI assistant? A: Restart the assistant, confirm the config file location, and ensure the package is installed globally.

Q: How to enable detailed logs? A: Set DEBUG=hiveflow-mcp:* in your environment before launching the server.

@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

• HiveFlow Documentation
• MCP Protocol Specification
• API Reference

🤝 Contributing

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

📄 License

MIT License - see LICENSE file for details.

🆘 Support

• GitHub Issues
• Discord Community
• Email Support

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Made with ❤️ by the HiveFlow team