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interactive-mcp

interactive-mcp

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github.com/ttommythAI & Agents351Updated 59w ago

About interactive-mcp

interactive-mcp is a locally run MCP server that lets an LLM ask the user questions through OS notifications and command-line prompts. It is aimed at users who want to confirm steps during AI-assisted coding, and the project is described as being in its early stages.

Key features

  1. 01Ask the user questions with optional predefined choices
  2. 02Send simple OS notifications
  3. 03Start, query and stop persistent command-line chat sessions
  4. 04Configurable timeout for user prompts
  5. 05Disable selected tools with a command-line flag

Use cases

  1. 01Gathering user feedback during code generation or modification
  2. 02Confirming actions or clarifying requirements in pair programming
  3. 03Waiting for user input or confirmation while an LLM is running

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"interactive": {
"command": "npx",
"args": [
"-y",
"interactive-mcp"
]
}
}
}
Run commandnpx -y interactive-mcp

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npminteractive-mcp

Tools

5

Saved tool list · updated 3w ago

ParameterTypeRequiredDescription
projectNamestringYesIdentifies the context/project making the request (used in prompt formatting)
messagestringYesThe specific question for the user (appears in the prompt)
predefinedOptionsarrayNoPredefined options for the user to choose from (optional)

Details

From the project README

interactive-mcp

npm version npm downloads smithery badge GitHub license code style: prettier Platforms GitHub last commit

Install MCP Server

Screenshot 2025-05-13 213745

A MCP Server implemented in Node.js/TypeScript, facilitating interactive communication between LLMs and users. Note: This server is designed to run locally alongside the MCP client (e.g., Claude Desktop, VS Code), as it needs direct access to the user's operating system to display notifications and command-line prompts.

(Note: This project is in its early stages.)

Want a quick overview? Check out the introductory blog post: Stop Your AI Assistant From Guessing — Introducing interactive-mcp

Demo Video

Tools

This server exposes the following tools via the Model Context Protocol (MCP):

  • request_user_input: Asks the user a question and returns their answer. Can display predefined options.
  • message_complete_notification: Sends a simple OS notification.
  • start_intensive_chat: Initiates a persistent command-line chat session.
  • ask_intensive_chat: Asks a question within an active intensive chat session.
  • stop_intensive_chat: Closes an active intensive chat session.

Demo

Here are demonstrations of the interactive features:

Normal QuestionCompletion Notification
Normal Question DemoCompletion Notification Demo
Intensive Chat StartIntensive Chat End
Start Intensive Chat DemoEnd Intensive Chat Demo

Usage Scenarios

This server is ideal for scenarios where an LLM needs to interact directly with the user on their local machine, such as:

  • Interactive setup or configuration processes.
  • Gathering feedback during code generation or modification.
  • Clarifying instructions or confirming actions in pair programming.
  • Any workflow requiring user input or confirmation during LLM operation.

Client Configuration

This section explains how to configure MCP clients to use the interactive-mcp server.

By default, user prompts will time out after 30 seconds. You can customize server options like timeout or disabled tools by adding command-line flags directly to the args array when configuring your client.

Please make sure you have the npx command available.

Usage with Claude Desktop / Cursor

Add the following minimal configuration to your claude_desktop_config.json (Claude Desktop) or mcp.json (Cursor):

{
  "mcpServers": {
    "interactive": {
      "command": "npx",
      "args": ["-y", "interactive-mcp"]
    }
  }
}

With specific version

{
  "mcpServers": {
    "interactive": {
      "command": "npx",
      "args": ["-y", "[email protected]"]
    }
  }
}

Example with Custom Timeout (30s):

{
  "mcpServers": {
    "interactive": {
      "command": "npx",
      "args": ["-y", "interactive-mcp", "-t", "30"]
    }
  }
}
Usage with VS Code

Add the following minimal configuration to your User Settings (JSON) file or .vscode/mcp.json:

{
  "mcp": {
    "servers": {
      "interactive-mcp": {
        "command": "npx",
        "args": ["-y", "interactive-mcp"]
      }
    }
  }
}
macOS Recommendations

For a smoother experience on macOS using the default Terminal.app, consider this profile setting:

  • (Shell Tab): Under "When the shell exits" (Terminal > Settings > Profiles > [Your Profile] > Shell), select "Close if the shell exited cleanly" or "Close the window". This helps manage windows when the MCP server starts and stops.

Development Setup

This section is primarily for developers looking to modify or contribute to the server. If you just want to use the server with an MCP client, see the "Client Configuration" section above.

Prerequisites
  • Node.js: Check package.json for version compatibility.
  • pnpm: Used for package management. Install via npm install -g pnpm after installing Node.js.
Installation (Developers)
  1. Clone the repository:

    git clone https://github.com/ttommyth/interactive-mcp.git
    cd interactive-mcp
    
  2. Install dependencies:

    pnpm install
    
Running the Application (Developers)
pnpm start
Command-Line Options

The interactive-mcp server accepts the following command-line options. These should typically be configured in your MCP client's JSON settings by adding them directly to the args array (see "Client Configuration" examples).

OptionAliasDescription
--timeout-tSets the default timeout (in seconds) for user input prompts. Defaults to 30 seconds.
--disable-tools-dDisables specific tools or groups (comma-separated list). Prevents the server from advertising or registering them. Options: request_user_input, message_complete_notification, intensive_chat.

Example: Setting multiple options in the client config args array:

// Example combining options in client config's "args":
"args": [
  "-y", "interactive-mcp",
  "-t", "30", // Set timeout to 30 seconds
  "--disable-tools", "message_complete_notification,intensive_chat" // Disable notifications and intensive chat
]

Development Commands

  • Build: pnpm build
  • Lint: pnpm lint
  • Format: pnpm format

Guiding Principles for Interaction

When interacting with this MCP server (e.g., as an LLM client), please adhere to the following principles to ensure clarity and reduce unexpected changes:

  • Prioritize Interaction: Utilize the provided MCP tools (request_user_input, start_intensive_chat, etc.) frequently to engage with the user.
  • Seek Clarification: If requirements, instructions, or context are unclear, always ask clarifying questions before proceeding. Do not make assumptions.
  • Confirm Actions: Before performing significant actions (like modifying files, running complex commands, or making architectural decisions), confirm the plan with the user.
  • Provide Options: Whenever possible, present the user with predefined options through the MCP tools to facilitate quick decisions.

You can provide these instructions to an LLM client like this:

# Interaction

- Please use the interactive MCP tools
- Please provide options to interactive MCP if possible

# Reduce Unexpected Changes

- Do not make assumption.
- Ask more questions before executing, until you think the requirement is clear enough.

Contributing

Contributions are welcome! Please follow standard development practices. (Further details can be added later).

License

MIT (See LICENSE file for details - if applicable, or specify license directly).

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.