Irrational

io.github.urbanmorph由 GitHub 账号 urbanmorph 发布v0.1.0更新于 Oct 25, 2026

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

已验证Streamable HTTP可网页运行知识与记忆AI 与机器学习

概览

AI 生成的概览

一个远程 MCP 服务器,为助手提供 22 种认知偏差目录和对抗式提示,用于审查决策中的认知偏差。

可以做什么
审查决策中的认知偏差,返回以结论开头的指令,由助手自身的模型执行并给出推理与成功概率估计,每项偏差都对应到用户原话,可选其他语言或结构化 JSON 输出(analyze_decision)。浏览 22 种偏差目录,可按类别筛选(list_biases)。按 id 查看单个偏差的完整条目,包括破解该偏差的提问和引用来源(get_bias)。
适用场景
适合希望对计划、估算或草稿推理做一次刻意唱反调的复核,让助手论证对立面而不是附和。也适合查询某个具名偏差及其破解提问。
运行要求
远程 streamable HTTP 端点;无需安装、API 密钥、环境变量或请求头。服务器本身不运行模型,推理由所连接的助手模型完成。
安装前请注意
服务器返回的是指令,由助手模型执行,因此审查质量取决于该模型。README 称不存储决策、数据不离开客户端,但决策文本会发送到远程端点以生成指令。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Irrational,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Web executable,通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体,或 SourceWeft 对话中。智能体会读取该服务器的安装指南,先向你展示发布者、端点和登录方式,经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/io.github.urbanmorph%2Firrational/install.md,并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前,先告诉我它的发布者、端点和登录方式,等我确认。不要把 API 密钥写进命令或我会提交的文件;问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "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.

来源: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.

输入参数modejudgment必填languagereasoningstructured

list_biases

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

输入参数family

get_bias

Get the full entry for one bias by id.

输入参数id必填

版本历史

1
  1. v0.1.0最新Sep 16, 2026

评分

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