Rubber Duck

v1.18.0Coding Agentsstable

An MCP server that acts as a bridge to query multiple OpenAI-compatible LLMs with MCP tool access. Just like rubber duck debugging, explain your problems to various AI 'ducks' who can actually research and get different perspectives!

mcp-rubber-duckmcpai-integration
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What is Rubber Duck?

Rubber Duck is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to mcp server that acts as a bridge to query multiple openai-compatible llms with mcp tool access. just like rubber duck debugging, explain your problems to various ai 'ducks' who can actually research a...

An MCP server that acts as a bridge to query multiple OpenAI-compatible LLMs with MCP tool access. Just like rubber duck debugging, explain your problems to various AI 'ducks' who can actually research and get different perspectives!

This server falls under the Coding Agents category on MCPgee, the world's largest MCP server directory with 33,000+ servers.

Features

  • ask_duck
  • chat_with_duck
  • clear_conversations
  • list_ducks
  • list_models

Use Cases

Query multiple OpenAI-compatible LLMs with MCP tool access.
Get diverse AI perspectives on your problems for better solutions.
nesquikm

Maintainer

LicenseMIT License
Languagetypescript
Versionv1.18.0
UpdatedMay 22, 2026
Statushealthy
Maintenanceactive

Works with

ClaudeOpenAIwindowsmacoslinux

Installation

NPM

npx -y mcp-rubber-duck

Manual Installation

npx -y mcp-rubber-duck

Configuration

Configuration Details

Config File

claude_desktop_config.json

Performance

Response Metrics

Response Time< 200ms
ThroughputMedium

Resource Usage

Memory UsageLow
CPU UsageLow

How to Set Up and Use Rubber Duck

MCP Rubber Duck is an MCP server that acts as a bridge to query multiple LLMs simultaneously — both OpenAI-compatible HTTP APIs (OpenAI, Gemini, Groq, Perplexity, Ollama, Azure OpenAI, and more) and CLI coding agents (Claude Code, Codex, Gemini CLI, Aider). Just like rubber duck debugging, it lets you explain your problem to various AI 'ducks' and get diverse perspectives. Advanced features include consensus voting, LLM-as-judge evaluation, structured debates (Oxford, Socratic, adversarial), iterative refinement, and MCP Bridge to give ducks access to other MCP servers.

Prerequisites

  • Node.js 20 or higher installed
  • At least one API key for an HTTP provider (OPENAI_API_KEY, GEMINI_API_KEY, or GROQ_API_KEY) OR a CLI agent (Claude Code, Codex, Gemini CLI) installed locally
  • An MCP-compatible client such as Claude Desktop, Cursor, or VS Code with MCP support
1

Install the package

Install mcp-rubber-duck globally from npm, or plan to run it directly via npx in your MCP client configuration.

npm install -g mcp-rubber-duck
2

Configure provider API keys

Set environment variables for the LLM providers you want to use as ducks. At minimum, set one of the following. You can also configure CLI agents like Claude Code by installing them locally.

export OPENAI_API_KEY=sk-...
export GEMINI_API_KEY=AI...
export GROQ_API_KEY=gsk_...
3

Add to your MCP client configuration

Add mcp-rubber-duck to your claude_desktop_config.json, passing your API keys as environment variables so the server can authenticate with LLM providers.

{
  "mcpServers": {
    "rubber-duck": {
      "command": "npx",
      "args": ["-y", "mcp-rubber-duck"],
      "env": {
        "OPENAI_API_KEY": "sk-...",
        "GEMINI_API_KEY": "AI...",
        "DEFAULT_PROVIDER": "openai"
      }
    }
  }
}
4

Verify available ducks

After starting your MCP client, use the list_ducks tool to confirm which providers are configured and healthy before starting a session.

5

Optionally enable CLI agent ducks

To use local CLI coding agents as ducks, set the corresponding environment variable and ensure the agent is installed. For example, to enable Claude Code as a duck:

export CLI_CLAUDE_ENABLED=true

Rubber Duck Examples

Client configuration

Configure Claude Desktop to use MCP Rubber Duck with OpenAI and Gemini as available ducks.

{
  "mcpServers": {
    "rubber-duck": {
      "command": "npx",
      "args": ["-y", "mcp-rubber-duck"],
      "env": {
        "OPENAI_API_KEY": "sk-your-openai-key",
        "GEMINI_API_KEY": "your-gemini-key",
        "DEFAULT_PROVIDER": "openai",
        "LOG_LEVEL": "info"
      }
    }
  }
}

Prompts to try

Example prompts that use the rubber duck tools to get multiple AI perspectives and run structured debates.

- "Ask the openai duck: what's the best approach for implementing rate limiting in a Node.js API?"
- "Compare what openai and gemini think about using microservices vs. a monolith for a startup"
- "Have all configured ducks vote on whether to use REST or GraphQL for this API"
- "Run a Socratic debate between two ducks about the tradeoffs of eventual consistency"
- "List all available ducks and their current health status"
- "Chat with the groq duck about my authentication bug — here's the stack trace: [paste trace]"

Troubleshooting Rubber Duck

list_ducks shows a provider as unhealthy or unavailable

Verify the API key for that provider is correctly set in the env block of your MCP config. Check the key has not expired and has sufficient quota. Use LOG_LEVEL=debug to see detailed error messages from the provider.

CLI duck (Claude Code, Codex) not appearing in list_ducks

Set CLI_CLAUDE_ENABLED=true (or CLI_CODEX_ENABLED=true) in your environment. Confirm the CLI agent binary is installed and accessible on your PATH by running 'which claude' or 'which codex' in your terminal.

npx -y mcp-rubber-duck fails with 'package not found' error

Run 'npm install -g mcp-rubber-duck' first to install the package globally. Alternatively, verify your npm registry is reachable and Node.js 20+ is installed with 'node --version'.

Frequently Asked Questions about Rubber Duck

What is Rubber Duck?

Rubber Duck is a Model Context Protocol (MCP) server that mcp server that acts as a bridge to query multiple openai-compatible llms with mcp tool access. just like rubber duck debugging, explain your problems to various ai 'ducks' who can actually research and get different perspectives! It connects AI assistants to external tools and data sources through a standardized interface.

How do I install Rubber Duck?

Install via npm with the command: npx -y mcp-rubber-duck. Then add the server configuration to your AI client's JSON config file (e.g., claude_desktop_config.json or .cursor/mcp.json).

Which AI clients work with Rubber Duck?

Rubber Duck works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.

Is Rubber Duck free to use?

Yes, Rubber Duck is open source and available under the MIT License license. You can use it freely in both personal and commercial projects.

Browse More Coding Agents MCP Servers

Explore all coding agents servers available in the MCPgee directory. Each server includes setup guides for Claude, Cursor, and VS Code.

Quick Config Preview

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

Add this to your claude_desktop_config.json or .cursor/mcp.json

Read the full setup guide →

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