Mcp Rubber Duck

Created By
nesquikm4 months ago
Query multiple LLMs in parallel from AI coding tools. Ask a question to OpenAI, Gemini, Groq, or any OpenAI-compatible API — and CLI agents like Claude Code, Codex, and Gemini CLI — then compare answers, run consensus votes, structured debates, and judge evaluations. Rubber duck debugging, but the ducks talk back.
Overview

Rubber duck debugging, but the ducks talk back. An MCP server that bridges to multiple LLMs so you can get different perspectives without leaving your editor.

Features

  • Multi-provider querying — Ask OpenAI, Gemini, Groq, Together AI, Ollama, or any OpenAI-compatible API in parallel
  • CLI agent integration — Spawn Claude Code, Codex, Gemini CLI, Grok, and Aider as "ducks" with full codebase access
  • Duck Council — Query all providers simultaneously and compare responses side by side
  • Consensus voting — Have ducks vote on the best approach, with confidence scores
  • Structured debates — Ducks argue for and against, then a judge picks the winner
  • Conversations — Maintain chat context across multiple messages
  • MCP Bridge — Ducks can use external MCP tools (web search, file access, etc.)
  • Health monitoring — Automatic failover when a provider goes down
  • Security controls — Rate limiting, token constraints, pattern blocking, PII redaction
  • Usage analytics — Track requests, tokens, and estimated costs per provider
  • Interactive UIs — Rich HTML panels for comparison, voting, and debate visualization

Installation

npx mcp-rubber-duck

Also available via Docker and the official MCP Registry.

Server Config

{
  "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"
      }
    }
  }
}
Project Info
Created At
4 months ago
Updated At
4 months ago
Author Name
nesquikm
Star
-
Language
-
License
-
Category

Recommend Servers

View All
Mnemom

5 hours ago
//beforeyouship — LLM Cost Modeling From Your Editor
@Indiegoing

Query realistic LLM cost models without leaving your editor. beforeyouship models the **true monthly cost** of an LLM app architecture — retries, prompt caching, batch discounts, infra overhead, and 3×/10× growth — across GPT-5.x, Claude, Gemini, DeepSeek, and more. Not a token calculator: a planning tool for the design phase, before you commit to a stack. **No API key needed to try it** — demo mode covers the six free-tier models. A Pro key from [beforeyouship.dev](https://beforeyouship.dev) unlocks the full 18-model catalog. ## What you can ask - "How much will a RAG chatbot cost at 10,000 requests/day?" - "Compare Claude Haiku vs Gemini Flash pricing for my workload" - "What's the cheapest model for a multi-step agent at scale?" - "Show me current per-token prices for Anthropic models" ## Tools ### `estimate_cost` Full cost model for an architecture at a given usage level. Returns Naive / Realistic / Worst Case monthly cost per model, 3×/10× growth scenarios, and an opinionated recommendation with reasoning. ### `get_model_prices` Current per-1M-token pricing — input, output, cached input, batch — with context windows and staleness metadata. ### `list_archetypes` Seven preset architecture patterns (simple chatbot, chatbot with history, RAG pipeline, multi-model router, coding assistant, document processor, multi-step agent) used as starting points for estimates. ## Setup **Claude Code:** ​```bash claude mcp add --transport http beforeyouship https://beforeyouship.dev/api/mcp ​``` **Cursor / other clients** — add a remote server: ​```json { "mcpServers": { "beforeyouship": { "type": "streamable-http", "url": "https://beforeyouship.dev/api/mcp" } } } ​``` Add an `Authorization: Bearer bys_...` header with a Pro key for the full catalog. ## Try it > Estimate the monthly cost of a RAG pipeline at 10,000 requests/day

4 hours ago