MCP Rubber Duck

What is MCP Rubber Duck about?

MCP Rubber Duck enables developers to explain problems to a panel of AI "ducks"—different LLM providers and local coding agents—and receive varied perspectives, votes, rankings, and collaborative refinements in real time.

How to use MCP Rubber Duck?

1. Install the package globally or run it directly:

   npx mcp-rubber-duck
   

2. Configure one or more providers via a .env file or config/config.json (API keys, default model, temperature, etc.).
3. Invoke built‑in tools such as ask_duck, compare_ducks, duck_vote, duck_debate, or use the interactive MCP Apps UI inside supported editors.
4. For production, run the server mode (MCP_SERVER=true) and connect any MCP‑compatible client.

Key Features of MCP Rubber Duck

• Universal OpenAI‑compatible HTTP API support (OpenAI, Gemini, Anthropic, Groq, etc.)
• Local CLI coding agents (Claude Code, Codex, Gemini CLI, Grok, Aider)
• Simultaneous querying of multiple providers (Duck Council)
• Consensus voting with reasoning and confidence scores
• LLM‑as‑judge, iterative refinement, and structured debate formats (Oxford, Socratic, adversarial)
• Vision input for image‑capable models
• Automatic failover and health monitoring
• Usage tracking, token counting, and cost estimation per provider
• Guardrails for rate‑limiting, token limits, pattern blocking, and PII redaction
• Granular security and session‑based approvals
• Rich interactive HTML panels via MCP Apps (compare, vote, debate, usage stats)
• MCP Bridge to connect to other MCP servers

Use Cases of MCP Rubber Duck

• Rubber‑duck debugging: Get diverse AI feedback on code errors or design questions.
• Model comparison: Evaluate responses from several LLMs side‑by‑side to choose the best.
• Consensus decision making: Run multi‑duck voting for architecture choices, trade‑off analysis, or risk assessments.
• Collaborative code generation: Use iterative refinement between two ducks to polish generated snippets.
• Security / red‑team analysis: Leverage the red_team prompt and debate format to uncover vulnerabilities.
• Cost‑aware experimentation: Track token usage and estimated costs across providers.
• Integration in IDEs: Embed interactive panels in VS Code, Cursor, Claude Desktop, etc., for seamless workflow.

FAQ from the MCP Rubber Duck

Q: Do I need an OpenAI key to use other providers? A: No. Any provider with an OpenAI‑compatible endpoint works; just set the corresponding API key (e.g., GEMINI_API_KEY).

Q: Can I run the server locally without internet? A: Yes, by configuring local HTTP providers such as Ollama or LM Studio and ensuring the CLI agents are installed.

Q: How does failover work? A: If a request to the primary provider fails or hits rate limits, the server automatically retries with the next configured provider.

Q: What environments are supported? A: Node 20+, npm or yarn. Docker images are also provided for containerized deployment.

Q: How are guardrails configured? A: Through the guardrails section in configuration.md, where you can set rate limits, token caps, regex blocklists, and PII redaction rules.

Q: Is there a way to approve tool usage on a per‑session basis? A: Yes. The get_pending_approvals and approve_mcp_request tools let administrators approve or deny MCP tool calls during a session.

MCP Rubber Duck

An MCP (Model Context Protocol) server that acts as a bridge to query multiple LLMs -- both OpenAI-compatible HTTP APIs and CLI coding agents. Just like rubber duck debugging, explain your problems to various AI "ducks" and get different perspectives!

Features

• Universal OpenAI Compatibility -- Works with any OpenAI-compatible API endpoint
• CLI Agent Support -- Use CLI coding agents (Claude Code, Codex, Gemini CLI, Grok, Aider) as ducks
• Multiple Ducks -- Configure and query multiple LLM providers simultaneously
• Conversation Management -- Maintain context across multiple messages
• Duck Council -- Get responses from all your configured LLMs at once
• Consensus Voting -- Multi-duck voting with reasoning and confidence scores
• LLM-as-Judge -- Have ducks evaluate and rank each other's responses
• Iterative Refinement -- Two ducks collaboratively improve responses
• Structured Debates -- Oxford, Socratic, and adversarial debate formats
• MCP Prompts -- 8 reusable prompt templates for multi-LLM workflows
• Vision Input -- Send images alongside prompts to vision-capable models (docs)
• Automatic Failover -- Falls back to other providers if primary fails
• Health Monitoring -- Real-time health checks for all providers
• Usage Tracking -- Track requests, tokens, and estimated costs per provider
• MCP Bridge -- Connect ducks to other MCP servers for extended functionality (docs)
• Guardrails -- Pluggable safety layer with rate limiting, token limits, pattern blocking, and PII redaction (docs)
• Granular Security -- Per-server approval controls with session-based approvals
• Interactive UIs -- Rich HTML panels for compare, vote, debate, and usage tools (via MCP Apps)
• Tool Annotations -- MCP-compliant hints for tool behavior (read-only, destructive, etc.)
• Structured Output -- outputSchema on tools returning structured JSON for client-side validation (Cursor, VS Code/Copilot)

Supported Providers

HTTP Providers (OpenAI-compatible API)

Any provider with an OpenAI-compatible API endpoint, including:

• OpenAI (GPT-5.1, o3, o4-mini)
• Google Gemini (Gemini 3, Gemini 2.5 Pro/Flash)
• Anthropic (via OpenAI-compatible endpoints)
• Groq (Llama 4, Llama 3.3)
• Together AI (Llama 4, Qwen, and more)
• Perplexity (Online models with web search)
• Anyscale, Azure OpenAI, Ollama, LM Studio, Custom

CLI Providers (Coding Agents)

Command-line coding agents that run as local processes:

• Claude Code (claude) -- Codex (codex) -- Gemini CLI (gemini) -- Grok CLI (grok) -- Aider (aider) -- Custom

See CLI Providers for full setup and configuration.

Quick Start

# Install globally
npm install -g mcp-rubber-duck

# Or use npx directly in Claude Desktop config
npx mcp-rubber-duck

Using Claude Desktop? Jump to Claude Desktop Configuration. Using Cursor, VS Code, Windsurf, or another tool? See the Setup Guide.

Installation

Prerequisites

• Node.js 20 or higher
• npm or yarn
• At least one API key for an HTTP provider, or a CLI coding agent installed locally

Install from NPM

npm install -g mcp-rubber-duck

Install from Source

git clone https://github.com/nesquikm/mcp-rubber-duck.git
cd mcp-rubber-duck
npm install
npm run build
npm start

Configuration

Create a .env file or config/config.json. Key environment variables:

Variable	Description
OPENAI_API_KEY	OpenAI API key
GEMINI_API_KEY	Google Gemini API key
GROQ_API_KEY	Groq API key
DEFAULT_PROVIDER	Default provider (e.g., openai)
DEFAULT_TEMPERATURE	Default temperature (e.g., 0.7)
LOG_LEVEL	debug, info, warn, error
MCP_SERVER	Set to true for MCP server mode
MCP_BRIDGE_ENABLED	Enable MCP Bridge (ducks access external MCP servers)
CUSTOM_{NAME}_*	Custom HTTP providers
CLI_{AGENT}_ENABLED	Enable CLI agents (CLAUDE, CODEX, GEMINI, GROK, AIDER)

Full reference: Configuration docs

Interactive UIs (MCP Apps)

Four tools -- compare_ducks, duck_vote, duck_debate, and get_usage_stats -- can render rich interactive HTML panels inside supported MCP clients via MCP Apps. Once this MCP server is configured in a supporting client, the UIs appear automatically -- no additional setup is required. Clients without MCP Apps support still receive the same plain text output (no functionality is lost). See the MCP Apps repo for an up-to-date list of supported clients.

Compare Ducks

Compare multiple model responses side-by-side, with latency indicators, token counts, model badges, and error states.

Duck Vote

Have multiple ducks vote on options, displayed as a visual vote tally with bar charts, consensus badge, winner card, confidence bars, and collapsible reasoning.

Duck Debate

Structured multi-round debate between ducks, shown as a round-by-round view with format badge, participant list, collapsible rounds, and synthesis section.

Usage Stats

Usage analytics with summary cards, provider breakdown with expandable rows, token distribution bars, and estimated costs.

Available Tools

Tool	Description
ask_duck	Ask a single question to a specific LLM provider
chat_with_duck	Conversation with context maintained across messages
clear_conversations	Clear all conversation history
list_ducks	List configured providers and health status
list_models	List available models for providers
compare_ducks	Ask the same question to multiple providers simultaneously
duck_council	Get responses from all configured ducks
get_usage_stats	Usage statistics and estimated costs
duck_vote	Multi-duck voting with reasoning and confidence
duck_judge	Have one duck evaluate and rank others' responses
duck_iterate	Iteratively refine a response between two ducks
duck_debate	Structured multi-round debate between ducks
mcp_status	MCP Bridge status and connected servers
get_pending_approvals	Pending MCP tool approval requests
approve_mcp_request	Approve or deny a duck's MCP tool request

Full reference with input schemas: Tools docs

Available Prompts

Prompt	Purpose	Required Arguments
perspectives	Multi-angle analysis with assigned lenses	problem, perspectives
assumptions	Surface hidden assumptions in plans	plan
blindspots	Hunt for overlooked risks and gaps	proposal
tradeoffs	Structured option comparison	options, criteria
red_team	Security/risk analysis from multiple angles	target
reframe	Problem reframing at different levels	problem
architecture	Design review across concerns	design, workloads, priorities
diverge_converge	Divergent exploration then convergence	challenge

Full reference with examples: Prompts docs

Development

npm run dev        # Development with watch mode
npm test           # Run all tests
npm run lint       # ESLint
npm run typecheck  # Type check without emit

Documentation

Topic	Link
Setup guide (all tools)	docs/setup.md
Full configuration reference	docs/configuration.md
Claude Desktop setup	docs/claude-desktop.md
All tools with schemas	docs/tools.md
Prompt templates	docs/prompts.md
CLI coding agents	docs/cli-providers.md
MCP Bridge	docs/mcp-bridge.md
Guardrails	docs/guardrails.md
Docker deployment	docs/docker.md
Provider-specific setup	docs/provider-setup.md
Usage examples	docs/usage-examples.md
Architecture	docs/architecture.md
Roadmap	docs/roadmap.md

Troubleshooting

Provider Not Working

1. Check API key is correctly set
2. Verify endpoint URL is correct
3. Run health check: list_ducks({ check_health: true })
4. Check logs for detailed error messages

Connection Issues

• For local providers (Ollama, LM Studio), ensure they're running
• Check firewall settings for local endpoints
• Verify network connectivity to cloud providers

Rate Limiting

• Configure failover to alternate providers
• Adjust max_retries and timeout settings
• See Guardrails for rate limiting configuration

Contributing

     __
   <(o )___
    ( ._> /
     `---'  Quack! Ready to debug!

We love contributions! Whether you're fixing bugs, adding features, or teaching our ducks new tricks, we'd love to have you join the flock.

Check out our Contributing Guide to get started.

Quick start for contributors:

1. Fork the repository
2. Create a feature branch
3. Follow our conventional commit guidelines
4. Add tests for new functionality
5. Submit a pull request

License

MIT License - see LICENSE file for details

Acknowledgments

• Inspired by the rubber duck debugging method
• Built on the Model Context Protocol (MCP)
• Uses OpenAI SDK for HTTP provider compatibility
• Supports CLI coding agents (Claude Code, Codex, Gemini CLI, Grok, Aider)

Changelog

See CHANGELOG.md for a detailed history of changes and releases.

Registry & Directory

• NPM Package: npmjs.com/package/mcp-rubber-duck
• Docker Images: ghcr.io/nesquikm/mcp-rubber-duck
• MCP Registry: Official MCP server io.github.nesquikm/rubber-duck
• Glama Directory: glama.ai/mcp/servers/@nesquikm/mcp-rubber-duck
• Awesome MCP Servers: Listed in the community directory

Support

• Report issues: https://github.com/nesquikm/mcp-rubber-duck/issues
• Documentation: https://github.com/nesquikm/mcp-rubber-duck/wiki
• Discussions: https://github.com/nesquikm/mcp-rubber-duck/discussions

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Happy Debugging with your AI Duck Panel!