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!

![npm version](https://www.npmjs.com/package/mcp-rubber-duck) ![Docker Image](https://github.com/nesquikm/mcp-rubber-duck/pkgs/container/mcp-rubber-duck) ![MCP Registry](https://registry.modelcontextprotocol.io)

Why direct provider integration? MCP's sampling primitive -- a server borrowing the host's model -- was deprecated in the 2026-07-28 spec RC in favor of servers integrating directly with LLM provider APIs. Rubber Duck has always worked this way (it brings its own ducks), so it's aligned with where the protocol is heading -- no migration required.

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)
Spec-Aligned by Design -- connects directly to provider APIs, the path the MCP 2026-07-28 spec recommends now that server-side sampling is deprecated (SEP-2577)

Supported Providers

HTTP Providers (OpenAI-compatible API)

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

OpenAI
Google Gemini
Anthropic (via OpenAI-compatible endpoints)
Groq (fast inference for open-weight models)
Together AI (broad open-weight model catalog)
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

Check API key is correctly set
Verify endpoint URL is correct
Run health check: list_ducks({ check_health: true })
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:

Fork the repository
Create a feature branch
Follow our conventional commit guidelines
Add tests for new functionality
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!

mcp-rubber-duck