📇 🏠 ☁️ - An MCP server that bridges to multiple OpenAI-compatible LLMs - your AI rubber duck debugging panel for explaining problems to various AI "ducks" and getting different perspectives
Install
npx -y directlyRequired environment variables
OPENAI_API_KEYSet in your MCP configGEMINI_API_KEYSet in your MCP configGROQ_API_KEYSet in your MCP configclaude_desktop_config.json
{
"mcpServers": {
"nesquikm-mcp-rubber-duck": {
"command": "npx",
"args": [
"-y",
"directly"
],
"env": {
"OPENAI_API_KEY": "<YOUR_OPENAI_API_KEY>",
"GEMINI_API_KEY": "<YOUR_GEMINI_API_KEY>",
"GROQ_API_KEY": "<YOUR_GROQ_API_KEY>"
}
}
}
}Add this to your Claude Desktop config file. Find it at ~/Library/Application Support/Claude/claude_desktop_config.json on macOS.
What this does
nesquikm/mcp-rubber-duck exposes a set of tools to Claude over the Model Context Protocol. After you add it to claude_desktop_config.json (snippet above) and restart Claude Desktop, those tools become callable inside any conversation. That makes it useful when you want Claude to reach into other AI providers and LLM tooling without copy-pasting context every turn.
Requirements
This server needs the following environment variables to be set before it can run:
OPENAI_API_KEY GEMINI_API_KEY GROQ_API_KEY
Set these via the env object in your MCP config (see claude_desktop_config.json snippet above).
Common use cases
- •Delegate reasoning to a different model or LLM provider from a Claude session
- •Pull live model documentation, rate limits, or usage data into context
- •Chain Claude with another agent or evaluation pipeline
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