An MCP server that connects multiple OpenAI-compatible LLMs with shared tool access. Inspired by rubber duck debugging, it lets users discuss problems with different AI models, gather diverse insights
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!
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.
- 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 -- T
From the project README.
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