Human-In-the-Loop MCP Server

What does “Human-In-the-Loop” MCP Server do?

The Human-In-the-Loop MCP Server is a Model Context Protocol (MCP) server designed to enable seamless interaction between AI assistants (like Claude) and human users through intuitive graphical user interface (GUI) dialogs. Its primary function is to bridge the gap between automated AI processes and human decision-making, providing real-time user input tools, options, confirmations, and feedback mechanisms. By integrating these interactive dialog tools, developers can build AI workflows that require human judgment, approvals, or data entry at critical points. The server supports cross-platform GUIs (Windows, macOS, Linux) and features such as non-blocking operation, health checks, advanced error handling, and modern UI/UX design. This makes it a powerful tool for enhancing the reliability, safety, and customizability of AI-driven applications by incorporating human oversight and collaboration directly into automated processes.

List of Prompts

No explicit prompt templates are mentioned in the repository files or documentation.

List of Resources

No explicit MCP resource primitives are listed or described in the repository files or documentation.

List of Tools

• Text Input
  Allows AI assistants to prompt users for text, numbers, or other data with validation.
• Multiple Choice
  Presents users with a set of options for single or multiple selections.
• Multi-line Input
  Enables the collection of longer responses, such as code snippets or detailed descriptions.
• Confirmation Dialogs
  Requests yes/no decisions from users before proceeding with critical actions.
• Information Messages
  Displays notifications, status updates, or results to the user.
• Health Check
  Provides a mechanism for monitoring server status and GUI availability.

Use Cases of this MCP Server

• Human-in-the-Loop Approval
  Integrate human approval steps into automated workflows, ensuring that critical actions (such as deployments, data modifications, or sensitive operations) require explicit user confirmation.
• Dynamic Data Collection
  Prompt users to provide specific data or feedback in real time, which can be incorporated into AI-driven processes for improved accuracy and customization.
• Interactive Troubleshooting
  Allow AI agents to escalate ambiguous or complex issues to a human operator through GUI dialogs, gathering additional context to resolve problems more effectively.
• Safety & Compliance Verification
  Require human verification for tasks that must comply with regulations or safety rules, reducing the risk of unauthorized or unsafe AI behavior.
• User Feedback & Iterative Design
  Collect structured feedback from end-users during testing or operation, enabling rapid iteration and refinement of AI workflows.

How to set it up

Windsurf

1. Ensure prerequisites like Node.js are installed.
2. Locate your configuration file (e.g., windsurf.config.json).
3. Add the Human-In-the-Loop MCP Server as an MCP server entry:

   {
     "mcpServers": [
       {
         "name": "human-in-the-loop",
         "command": "npx",
         "args": ["@human-in-the-loop/mcp-server@latest"]
       }
     ]
   }
   

4. Save your configuration and restart Windsurf.
5. Verify the setup by checking for the server in your MCP panel.

Claude

1. Ensure Claude supports external MCP servers.
2. Locate the MCP integration settings.
3. Add the server using the following JSON:

   {
     "mcpServers": [
       {
         "name": "human-in-the-loop",
         "command": "npx",
         "args": ["@human-in-the-loop/mcp-server@latest"]
       }
     ]
   }
   

4. Save and reload the Claude environment.
5. Verify the MCP server is active and accessible.

Cursor

1. Install Node.js and required dependencies.
2. Open the Cursor settings or configuration file.
3. Insert the MCP server entry:

   {
     "mcpServers": [
       {
         "name": "human-in-the-loop",
         "command": "npx",
         "args": ["@human-in-the-loop/mcp-server@latest"]
       }
     ]
   }
   

4. Restart Cursor to apply changes.
5. Confirm that the Human-In-the-Loop MCP Server is listed.

Cline

1. Verify Cline is installed and supports MCP plugins.
2. Edit your cline.config.json file.
3. Add the following MCP server configuration:

   {
     "mcpServers": [
       {
         "name": "human-in-the-loop",
         "command": "npx",
         "args": ["@human-in-the-loop/mcp-server@latest"]
       }
     ]
   }
   

4. Save the file and restart Cline.
5. Ensure the MCP server is running by checking in the UI.

Securing API Keys

To secure API keys and sensitive inputs, use environment variables in your JSON configuration as follows:

{
  "mcpServers": [
    {
      "name": "human-in-the-loop",
      "command": "npx",
      "args": ["@human-in-the-loop/mcp-server@latest"],
      "env": {
        "API_KEY": "${HITL_API_KEY}"
      },
      "inputs": {
        "apiKey": "${HITL_API_KEY}"
      }
    }
  ]
}

Replace ${HITL_API_KEY} with your actual environment variable name.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "human-in-the-loop": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “human-in-the-loop” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion

The Human-In-the-Loop MCP Server offers a well-defined set of interactive tools bridging AI automation with human oversight, but lacks explicit prompt and resource definitions. Its documentation is clear, and it supports secure setup and tool primitives. Rating: 6/10.

MCP Score