@nesquikm
基本情報
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!
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"rubber-duck": {
"command": "mcp-rubber-duck",
"env": {
"MCP_SERVER": "true",
"OPENAI_API_KEY": "<YOUR_OPENAI_KEY>",
"GEMINI_API_KEY": "<YOUR_GEMINI_KEY>",
"DEFAULT_PROVIDER": "openai"
}
}
}
}Ask a single question to a specific LLM provider
Conversation with context maintained across messages
Clear all conversation history
List configured providers and health status
List available models for providers
Ask the same question to multiple providers simultaneously
Get responses from all configured ducks
Usage statistics and estimated costs
Multi-duck voting with reasoning and confidence
Have one duck evaluate and rank others' responses
Iteratively refine a response between two ducks
Structured multi-round debate between ducks
MCP Bridge status and connected servers
Pending MCP tool approval requests
Approve or deny a duck's MCP tool request
MCP Rubber Duck is an MCP server that bridges to multiple LLMs—both OpenAI-compatible HTTP APIs and CLI coding agents—inspired by rubber duck debugging. It lets you explain problems to various AI “ducks” and get different perspectives.
Install globally via npm install -g mcp-rubber-duck or use npx directly. Configure API keys in a .env file or config/config.json. Invoke by running the server and using the provided tools (e.g., ask_duck, compare_ducks, duck_debate) from an MCP client. For Claude Desktop, see the dedicated setup guide.
MCP Rubber Duck uses direct LLM provider integration, which aligns with the 2026-07-28 spec where server-side sampling was deprecated. It brings its own ducks and does not require the host’s model.
Node.js 20 or higher, npm or yarn, and at least one API key for an HTTP provider or a local CLI coding agent.
OpenAI, Google Gemini, Anthropic, Groq, Together AI, Perplexity, Anyscale, Azure OpenAI, Ollama, LM Studio, and custom OpenAI-compatible endpoints. CLI agents: Claude Code, Codex, Gemini CLI, Grok, Aider, and custom.
Data is handled locally by the server; conversation history is maintained in memory and can be cleared via `clear_conversations`. API keys are stored in environment variables or config files.
The server uses MCP stdio transport by default. Authentication is via API keys configured for each provider; no built-in auth for the server itself.
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