interactive-mcp MCP Server

What does “interactive-mcp” MCP Server do?

The interactive-mcp MCP (Model Context Protocol) Server is a local, cross-platform tool designed to facilitate seamless interaction between AI agents and users. Its primary purpose is to enable “human-in-the-loop” workflows, allowing developers and AI assistants to work together interactively. By acting as a bridge, interactive-mcp connects AI models with external systems, such as databases, files, or APIs, thereby enhancing development productivity. It is particularly suited for environments where real-time feedback or intervention is necessary, supporting various platforms and providing an extensible foundation for integrating custom actions, prompt templates, and resource exposure. This empowers developers to build more dynamic, context-aware AI-powered tools and workflows.

List of Prompts

No information about prompt templates was found in the repository.

List of Resources

No information about specific resources exposed by the interactive-mcp server was found in the repository.

List of Tools

No explicit list of tools could be determined from the available files or documentation in the repository.

Use Cases of this MCP Server

• Human-in-the-Loop Coding
  Allows developers to interact directly with AI agents, providing real-time feedback, validation, and corrections during coding sessions.
• Cross-Platform Development
  Enables AI-driven workflows on multiple platforms, supporting diverse developer environments and operating systems.
• AI-Augmented Code Review
  Facilitates collaborative code review sessions where both humans and AI agents can inspect, annotate, and improve code interactively.
• Custom Integration Prototyping
  Serves as a foundation for building new tools and integrations that require both automated AI actions and human decision points.
• Enhanced Productivity Workflows
  Streamlines tasks such as code generation, refactoring, or documentation by allowing seamless switching between AI automation and human control.

How to set it up

Windsurf

1. Prerequisites: Ensure Node.js is installed.
2. Locate Configuration: Open your Windsurf configuration file (e.g., windsurf.config.json).
3. Add interactive-mcp Server: Insert the server entry in the mcpServers object.
4. Save and Restart: Save changes and restart Windsurf.
5. Verify Setup: Check logs/output for successful registration.

Example JSON Configuration:

{
  "mcpServers": {
    "interactive-mcp": {
      "command": "npx",
      "args": ["@ttommyth/interactive-mcp@latest"]
    }
  }
}

Claude

1. Prerequisites: Install Node.js and Claude as required.
2. Locate Configuration: Open Claude’s MCP configuration (e.g., claude.config.json).
3. Add interactive-mcp Server: Add to the mcpServers section.
4. Save and Restart: Save file and restart Claude.
5. Verify Setup: Confirm connection in Claude’s interface.

Example JSON Configuration:

{
  "mcpServers": {
    "interactive-mcp": {
      "command": "npx",
      "args": ["@ttommyth/interactive-mcp@latest"]
    }
  }
}

Cursor

1. Prerequisites: Ensure Node.js is available.
2. Locate Configuration: Edit Cursor’s MCP configuration file.
3. Add interactive-mcp Server: Update the mcpServers object.
4. Save and Restart: Save and restart Cursor.
5. Verify Setup: Confirm server registration in UI or logs.

Example JSON Configuration:

{
  "mcpServers": {
    "interactive-mcp": {
      "command": "npx",
      "args": ["@ttommyth/interactive-mcp@latest"]
    }
  }
}

Cline

1. Prerequisites: Install Node.js.
2. Locate Configuration: Find cline.config.json or equivalent.
3. Add interactive-mcp Server: Insert server details under mcpServers.
4. Save and Restart: Save file and restart Cline.
5. Verify Setup: Ensure the server is listed as active.

Example JSON Configuration:

{
  "mcpServers": {
    "interactive-mcp": {
      "command": "npx",
      "args": ["@ttommyth/interactive-mcp@latest"]
    }
  }
}

Securing API Keys Using Environment Variables:

Add sensitive variables using the env property:

{
  "mcpServers": {
    "interactive-mcp": {
      "command": "npx",
      "args": ["@ttommyth/interactive-mcp@latest"],
      "env": {
        "API_KEY": "${API_KEY_FROM_ENV}"
      },
      "inputs": {
        "apiKey": "${API_KEY_FROM_ENV}"
      }
    }
  }
}

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:

{
  "interactive-mcp": {
    "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 “interactive-mcp” to the name of your MCP server if different, and replace the URL with your MCP server’s URL.

Overview

Our opinion

While interactive-mcp presents a promising human-in-the-loop approach and is actively developed with good adoption (stars/forks), the lack of explicit documentation on prompts, tools, and resources limits its immediate usability for advanced MCP workflows. Its setup is straightforward and well-supported for common platforms.

MCP Score