An MCP server that bridges to multiple OpenAI-compatible LLMs - your AI rubber duck debugging panel
If you already use MCP Rubber Duck, the mcp rubber duck mcp server is the piece that lets your assistant work with it directly. An MCP server that bridges to multiple OpenAI-compatible LLMs - your AI rubber duck debugging panel.
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!
The toolset is worth reading before you wire it up, because it tells you what the integration is really for:
OpenAI — Google Geminiask_duck — Ask a single question to a specific LLM providerchat_with_duck — Conversation with context maintained across messagesclear_conversations — Clear all conversation historylist_ducks — List configured providers and health statuslist_models — List available models for providerscompare_ducks — Ask the same question to multiple providers simultaneouslyduck_council — Get responses from all configured ducksget_usage_stats — Usage statistics and estimated costsduck_vote — Multi-duck voting with reasoning and confidenceduck_judge — Have one duck evaluate and rank others' responsesduck_iterate — Iteratively refine a response between two ducksConfiguration is passed through the environment: OPENAI_API_KEY, GEMINI_API_KEY, GROQ_API_KEY. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
Installation goes through your MCP client rather than a global install: point it at directly on npm and it is fetched when the client starts. The copy-paste blocks for Claude Desktop, Claude Code and Cursor are further down this page.
Among the AI and media services options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. MCP Rubber Duck's toolset — OpenAI, ask_duck, chat_with_duck and 11 more — is a fair guide to whether it matches your workflow. It is maintained by nesquikm; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| OpenAI | **Google Gemini** |
| 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 |
{
"mcpServers": {
"rubber-duck": {
"command": "npx",
"args": ["-y", "directly"],
"env": {
"OPENAI_API_KEY": "your-value",
"GEMINI_API_KEY": "your-value",
"GROQ_API_KEY": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
| GEMINI_API_KEY | Credential the server authenticates with. | Yes |
| GROQ_API_KEY | Credential the server authenticates with. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.