Irrational

io.github.urbanmorph由 GitHub 帳號 urbanmorph 發布v0.1.0更新於 Oct 25, 2026

Adversarial behavioural-bias engine — audits your decisions for cognitive biases via your own AI.

已驗證Streamable HTTP可網頁執行知識與記憶AI 與機器學習

概覽

AI 產生的概覽

一個遠端 MCP 伺服器,為助理提供 22 種認知偏誤目錄與對抗式提示,用來審查決策中的認知偏誤。

可以做什麼
審查決策中的認知偏誤,回傳以結論開頭的指令,由助理自身的模型執行並給出推理與成功機率估計,每項偏誤都對應到使用者原話,可選其他語言或結構化 JSON 輸出(analyze_decision)。瀏覽 22 種偏誤目錄,可依類別篩選(list_biases)。依 id 查看單一偏誤的完整條目,包括破解該偏誤的提問與引用來源(get_bias)。
適用情境
適合想對計畫、估算或草稿推理做一次刻意唱反調的複核,讓助理論證對立面而不是附和。也適合查詢某個具名偏誤及其破解提問。
執行需求
遠端 streamable HTTP 端點;無需安裝、API 金鑰、環境變數或請求標頭。伺服器本身不執行模型,推理由所連接的助理模型完成。
安裝前請注意
伺服器回傳的是指令,由助理模型執行,因此審查品質取決於該模型。README 稱不儲存決策、資料不離開用戶端,但決策文字會傳送到遠端端點以產生指令。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Irrational,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Web executable,透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。

交給代理程式安裝

把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式,或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南,先向你展示發布者、端點和登入方式,經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。

閱讀 https://sourceweft.com/mcp/io.github.urbanmorph%2Firrational/install.md,並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前,先告訴我它的發布者、端點和登入方式,等我確認。不要把 API 金鑰寫進命令或我會提交的檔案;問我金鑰應從哪裡取得。

檢視代理程式所依據的安裝指南

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。

{
  "mcpServers": {
    "irrational": {
      "type": "http",
      "url": "https://irrational.pages.dev/mcp/server"
    }
  }
}

README

Irrational

A field guide to the mistakes your mind makes, and an open, adversarial behavioural-bias engine you can call from your own AI or any MCP agent.

[Irrational: a field guide to the mistakes your mind makes, and an engine that audits your decisions]

[Live] [MCP Registry] [CI] [License: MIT] [PRs welcome]

→ irrational.pages.dev · Field guide · Connect via MCP · Sources

Irrational runs no AI model. It provides the curated adversarial framework, a 22-bias catalogue, and a stateless scaffold, delivered as a public field guide and an open MCP tool, and the reasoning is done by whatever model you or your agent already have. No API keys. No stored decisions. Nothing leaves you.

A frontier model already knows the biases, so the value isn't informational; it's behavioural. A plain LLM is trained to agree, while Irrational has a fixed adversarial contract (assume you're biased, refuse to validate, argue the other side) that an agent can invoke systematically, even on its own draft reasoning.

What an audit looks like

You: "We should rewrite our 6-year-old backend from scratch this year, we've already spent two months on a POC that went well, and I'm confident we can finish in six."

THE CALL: Don't greenlight the big-bang rewrite. Carve off the worst module first, with a hard 3-month checkpoint. Confidence the 6-month plan succeeds: ~25%, mostly sunk cost + the planning fallacy.

THE AUDIT: Those "two months on a POC" are gone either way, but they're doing most of the talking (sunk cost). "About six months" is a best-case with no reference class; rewrites of mature systems routinely run 2–3× (planning fallacy)…

Verdict-first, every bias tied to your own words, grounded in a cited catalogue. See the full worked examples →

Add it to your AI

Listed on the official MCP Registry as io.github.urbanmorph/irrational. Connect any MCP client to the live endpoint, no install, no keys:

bash
# Claude Codeclaude mcp add --transport http irrational https://irrational.pages.dev/mcp/server

…or point any MCP client at https://irrational.pages.dev/mcp/server:

json
{  "mcpServers": {    "irrational": {      "type": "http",      "url": "https://irrational.pages.dev/mcp/server"    }  }}

Tools: analyze_decision (the adversarial audit) · list_biases · get_bias. Pass language for a reply in another language, or structured: true for parseable JSON.

What's inside

  • Field guide: 22 cognitive biases grouped by Buster Benson's four problems the brain solves, each with the question that breaks its spell and a verified citation.
  • The engine: an MCP server exposing analyze_decision, list_biases, get_bias. analyze_decision returns a verdict-first directive your model executes. The discipline is ours; the reasoning is yours.
  • The web forge: type a decision; it assembles the adversarial prompt in your browser and hands off to your AI. No model runs here.
  • Pen-and-paper audit: the whole method on one printable page.

Stack

Astro (pure static) · Cloudflare Pages · a hand-rolled, dependency-free JSON-RPC MCP endpoint as a Pages Function. No runtime model, no database. Single source of truth: src/data/biases.ts feeds both the web and the MCP.

Develop

bash
npm install --ignore-scripts   # we don't use image optimization, so skip sharp's native buildnpm run dev                    # http://localhost:4321npm test                       # vitest, engine + catalogue (29 tests)npm run build                  # static pages

The MCP endpoint is a Cloudflare Pages Function, run it locally with npx wrangler pages dev dist.

Contributing

Translations, proposed biases, and worked examples are welcome, see CONTRIBUTING.md. This project also publishes a public PDGI scorecard.

Licence

Code is MIT. The bias-catalogue text (definitions, examples, and the curated prose in src/data/) is additionally available under CC-BY-4.0. Every claim is cited, see /sources.

Built by Urban Morph.

來源:README.md,提交 bb7f8b4

工具

3

analyze_decision

Adversarially audit a decision for cognitive biases. Returns a directive YOUR model executes to produce the composed audit (verdict-first). Provide reasoning, not just the conclusion.

輸入參數modejudgment必填languagereasoningstructured

list_biases

List the 22-bias catalogue, optionally filtered by family.

輸入參數family

get_bias

Get the full entry for one bias by id.

輸入參數id必填

版本歷史

1
  1. v0.1.0最新Sep 16, 2026

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