HiveFlow▌

by hiveflowai
Streamline business management processes with HiveFlow's workflow automation software. Trigger automated workflows and a
Integrates with HiveFlow's workflow automation platform to enable triggering automated workflows, managing business processes, and executing workflow operations directly through conversational interactions.
best for
- / Business process automation teams
- / Developers building workflow integrations
- / Operations teams managing automated tasks
capabilities
- / Create and configure automation flows
- / Execute flows with custom inputs
- / Pause and resume active workflows
- / List and monitor flow executions
- / Manage MCP server configurations
- / Access flow execution history
what it does
Connects AI assistants to HiveFlow's automation platform for managing and executing business workflows through conversation.
about
HiveFlow is a community-built MCP server published by hiveflowai that provides AI assistants with tools and capabilities via the Model Context Protocol. Streamline business management processes with HiveFlow's workflow automation software. Trigger automated workflows and a It is categorized under ai ml, developer tools.
how to install
You can install HiveFlow in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
license
MIT
HiveFlow is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
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
FAQ
- What is the HiveFlow MCP server?
- HiveFlow is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for HiveFlow?
- This profile displays 10 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.
Ratings
4.5★★★★★10 reviews- ★★★★★Shikha Mishra· Oct 10, 2024
HiveFlow is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Piyush G· Sep 9, 2024
We evaluated HiveFlow against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Chaitanya Patil· Aug 8, 2024
Useful MCP listing: HiveFlow is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Sakshi Patil· Jul 7, 2024
HiveFlow reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Ganesh Mohane· Jun 6, 2024
I recommend HiveFlow for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Oshnikdeep· May 5, 2024
Strong directory entry: HiveFlow surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Dhruvi Jain· Apr 4, 2024
HiveFlow has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Rahul Santra· Mar 3, 2024
According to our notes, HiveFlow benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Pratham Ware· Feb 2, 2024
We wired HiveFlow into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Yash Thakker· Jan 1, 2024
HiveFlow is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.