House of Wisdom MCP — multi-model consultation council. Diverse AI model families investigate independently and return their own perspectives; the orchestrator weighs them. Inspired by the medieval Ba
What this is — an MCP (Model Context Protocol) server that asks the same question to several different AI model families at once and hands you back every answer, unmerged.
How to read it — What it is → Quick start → The three modes → The two tools → How a call flows → Installation → Configuration. Go to Sharp edges when something surprises you.
Requires — Python 3.10+, uv/uvx, and at least two enabled models.
Reflects code as of — 2026-07-21, master, package version 0.7.0.
The medieval Bayt al-Hikma worked because it was diverse: scholars, translators, and copyists from many traditions read the same questions through different lenses, and the reader weighed the results. This server does the same with models — OpenAI, Anthropic and Google via OpenRouter, DeepSeek, local Ollama, or any OpenAI-compatible endpoint.
It does It does not --- --- Fire N models in parallel on one question Merge, rank, vote, or summarize their answers Return one complete, self-contained analysis per model Return a single "council answer" Optionally let each model read your codebase first (read-only) Ever write, execute, or network beyond each model's own endpoint Tag each answer with the mode it was asked to run in Tell you which answer is correct
There is no synthesizer. The caller — your IDE agent — reads every perspective and decides. Treating any single perspective as ground truth defeats the design.
When it earns its cost. A call spends several model API calls and tens of seconds, so it pays off when a different model family seeing the problem would plausibly change the outcome: a contest
From the project README.
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