Tokonomix Council

Name: Tokonomix Council
Author: TokonoMix

作者：TokonoMix版本 1.14.0i★ –i在你的电脑上运行

注册名称ai.tokonomix/council

安装

概览

作者提供的说明（英文原文）

Multi-model consensus: 2-6 frontier LLMs answer, an independent judge synthesises one answer.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 搜索与 AI 记忆
你需要填写：TOKONOMIX_API_KEY

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
需要你自己填写： TOKONOMIX_API_KEY

标为机密的值是你账户的钥匙。千万不要分享给别人，也不要粘贴到聊天里。

你需要

npm已安装 Node.js（用于 npx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "council": {
      "command": "npx",
      "args": [
        "-y",
        "tokonomix-council-mcp"
      ],
      "env": {
        "TOKONOMIX_API_KEY": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add council --env TOKONOMIX_API_KEY=your-value -- npx -y tokonomix-council-mcp

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "council": {
      "command": "npx",
      "args": [
        "-y",
        "tokonomix-council-mcp"
      ],
      "env": {
        "TOKONOMIX_API_KEY": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "council": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "tokonomix-council-mcp"
      ],
      "env": {
        "TOKONOMIX_API_KEY": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上，无需账户。

需要你填写的值

TOKONOMIX_API_KEY 机密 env
Tokonomix API key (tok_live_...). Optional - keyless onboarding works without it.

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日

✓ 通过
作者与仓库一致
注册名称已通过域名 tokonomix.ai 验证（注册中心会核实域名所有权）
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表

与“搜索与 AI 记忆”中的其他服务器比较i

星标数多于此分类中其他 5654 个服务器的 0%
发布到注册中心的时间比此分类中其他 5654 个服务器的 51% 更新
基础检查结果等于或优于此分类中其他 5654 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

Tokonomix Council 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

你需要填写：TOKONOMIX_API_KEY

它在我的电脑上运行还是在云端？

它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（1.14.0）于40天前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。

有什么可以替代它？

同一分类中有：Context7, Codebase Memory, Exa

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Context7

作者：upstash

Up-to-date docs for AI agents

电脑或云端★ 64,224MIT昨天更新

安装

Codebase Memory

作者：DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

在你的电脑上运行★ 48,148MIT21天前更新

安装

Exa

作者：exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

在云端运行★ 5,213MIT2个月前更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

tokonomix-council-mcp

A multi-model consensus MCP server — cross-vendor proposers, an independent judge, and blind-spot/disagreement detection for high-stakes AI decisions. An open reference implementation of AI Decision Engineering — a discipline we are proposing, not yet an external standard.

Mission. *Help autonomous systems make better decisions by combining independent expert reasoning instead of trusting a single model.*

In 20 seconds: *Important AI decisions deserve more than one opinion.*

The Tokonomix ecosystem (where Council fits)

Council is one specialized component in a larger stack. Each owns exactly one responsibility, so they compose cleanly:

   Execution    →  ANS         (run a backlog to completion, unattended)

Decision → Council (independent review → grounded decision) ◀── this repo Verification → Media QC (image / video / document output quality) Measurement → Benchmark (intelligence tests, arena, reputation) Routing → Router (which provider/model hosts a call) Memory → Memory (durable context across sessions) Safety → Safety (policy / guardrail enforcement)

Council owns one box: the decision. It does not execute, schedule, route, or remember — those live in their own components (details below).

Status — what is live right now

So you never call something that isn't there yet. Server-gated features can be off for your account/tier; when they are, the tool returns a clear "not enabled" message instead of erroring.

Capability	Status
`tokonomix_consensus_ask` — modes `consensus` / `diff` / `best_of` / `raw` / `full`	Live
`tokonomix_single_ask` — single-model passthrough	Live
`tokonomix_list_models`, `tokonomix_get_balance`	Live
`tokonomix_skill_version`, `tokonomix_get_skill`	Live
`tokonomix_onboard`, `tokonomix_onboard_verify` — keyless first-run	Live
`tokonomix_rate_consensus` — agent rating / feedback-loop	Live (platform-gated)
`tokonomix_relay_human_feedback` — human verdict channel	Live (platform-gated)
Vision input (`images` on ask)	Live
`tokonomix_upload` + `context.session/handles` — large shared context-pack	Account-gated (on for eligible accounts)
`context.inline` / `context.github_refs` grounding	Live (account-gated)
Grounding-gate (`needs_context` refusal of thin input)	Gated (shadow-first rollout)
Reranking, web-search plugin	Planned

Council MCP exists to improve decision quality, not to chat, generate, or route. The governing idea is simple:

### Consensus × Ground Truth = Decision Quality

Consensus is one mechanism for decision quality — not the whole of it. Grounding (checking a claim against the real artifact) is the strongest lever, stronger than adding more models; judge-independence and verification are mechanisms too. Several independent expert models assess the same problem in parallel and blind, an independent, cross-family judge reconciles their findings, and grounding ties the whole thing to reality — so you get one decision you can act on, with the disagreements a single model would have hidden surfaced rather than smoothed away. **Consensus is a *means*; better decisions are the goal.**

It is not a chatbot, a model, an LLM, a prompt library, a multi-model chat, a voting system, or an averaging system. It does not make any model smarter, and it does not pick a winner by majority — it changes how multiple independent models' outputs become *one defensible decision*.

What it honestly buys you — and what it does not. Across our own validation (see Benchmarks), the council ties the best single model on clean accuracy and beats it nowhere, and on a SWE-bench bug-detection run it showed no measurable net uplift in catch rate over re-running a single agent. So we do not claim "more accurate" or "catches more bugs" anywhere. What it does buy is variance-elimination (you stop gambling on which single model you happened to ask), a verification / agent-QA layer, judge-independence, EU data residency, and grounding. Read When consensus helps / doesn't / can hurt before you rely on it — that section is the point, not a disclaimer.

Works with Claude Code, Cursor, Cline, Continue, and Zed. One npx line to install.

### Principles of Decision Engineering

The discipline Council implements rests on ten principles. An autonomous system should:

1. Verify before acting — a decision is provisional until it has been checked.

2. Preserve disagreement — the lone dissent is often the finding that mattered; never average it away.

3. Separate execution from judgement — the thing that *does* the work should not also be the thing that *grades* it.

4. Separate reasoning from verification — generating an answer and checking it are different jobs.

5. Separate confidence from correctness — a model's tone is identical whether it is right or wrong.

6. Prefer evidence over agreement — agreement measures agreement, not truth; ground the claim.

7. Expose uncertainty — surface what was *not* settled, don't hide it behind one confident answer.

8. Make decisions auditable — record who proposed, who judged, what it cost, on what input.

9. Remain reproducible — the same input and method should be re-runnable to the same finding.

10. Never hide failure modes — a decision tool that conceals when it fails is not a decision tool.

What Council owns — and what it explicitly does not

Council is a Decision Engineering layer. Its scope is deliberately narrow, because a clear boundary is what makes a component trustworthy to compose with.

Council owns DECISIONS ONLY:

decision quality and decision confidence,
independent review and reasoned consensus,
verification of reasoning and claims,
grounding (checking a claim against the real artifact),
disagreement analysis,
judge independence,
auditability of how a decision was reached.

Council is explicitly NOT responsible for: execution, workflow, task scheduling, agent autonomy, long-term memory, provider routing, or browser automation. Those are real responsibilities — they simply live in other components.

Need something executed (a backlog run to completion, unattended)? That is [Agents Never Sleep (ANS)](https://github.com/TokonoMix/agents-never-sleep), the execution governor. ANS is the worker/governor; Council is the verifier it can delegate a decision to. Council does not run, schedule, or remember work.

See Council in the Tokonomix ecosystem for where every adjacent responsibility lives.

Why Council exists (the short story — for a human OR an AI agent)

Why does Council exist? Because important AI decisions are made by single models that answer with total confidence and never tell you what they didn't consider. For chat that is fine; for a security review, a migration, a GDPR interpretation, or a legal clause, a confidently-wrong decision is expensive and you find out too late.

Why is one model not enough? A single model is one opinion with one blind spot. *Which* model is strongest is not stable — it shifts per task, per domain, per bug class. Standardise on the wrong one and you ship real defects without ever seeing a disagreement.

When does combining models help? When errors are *uncorrelated*: different vendors are wrong about different things, so you only need one of them to catch the timing side-channel or the missed edge case for it to surface. That decorrelation only exists across vendors — a single vendor's "panel" shares its own training data and blind spots.

When does it NOT help? On routine work (renames, formatting, lookups) and on easy tasks any strong single model already saturates. There the council only adds cost and noise — use a single model.

Why does grounding matter more than more models? Because agreement measures agreement, not truth. Ungrounded, frontier models can agree *and be uniformly wrong* (they share training data). Feeding the council the real artifact — the literal file, diff, spec, logs — is the lever that turns "they agreed" into "they checked it against reality." More models cannot fix a shared blind spot; grounding can.

Why does judge independence matter? Because a judge that also wrote one of the answers scores its own work and *inflates* agreement (LLM self-preference is real and measured). A disjoint, cross-family judge is the only honest reconciler.

Why do failure modes matter? Because consensus *can* be worse than a single model — lossy synthesis, correlated failures, false consensus, specialist overlap. A decision tool that hides its failure modes is not a decision tool. Honesty about when consensus does NOT work is the competitive advantage, not a caveat we bury.

Why Decision Engineering is becoming necessary
Why LLMs cannot solve this themselves
Design principles
A story: the security review
The problem
Why single-model review falls short
How Council works
Architecture
The consensus process
The judge
When consensus helps / doesn't / can hurt
Decision Engineering vs existing techniques
Council in the Tokonomix ecosystem
Practical examples
Installation
Integration & API surfaces
Benchmarks
Glossary
FAQ
Roadmap

Going deeper: this README is the field guide. The full argument — why the field exists, the design principles, the failure modes, why one model is insufficient, why consensus *alone* is insufficient, why grounding is necessary, and why a human stays ultimately accountable — lives in the [Decision Engineering Manifesto](https://github.com/TokonoMix/tokonomix-council-mcp/blob/main/docs/decision-engineering-manifesto.md). The README links out rather than duplicating that depth.

Why Decision Engineering is becoming necessary

Every time software took on more responsibility, a new discipline appeared to keep it trustworthy — not because engineers got worse, but because the cost of an unchecked output got higher:

Software gained…	…so we invented
Code that ran unattended	Testing — prove it does what we think
Many people committing	Continuous Integration — catch breakage on every change
Code too large to hold in one head	Code Review — a second pair of eyes before merge
Subtle, repeating defect classes	Static Analysis — a machine that flags them automatically
Systems shipping continuously	DevOps — make deployment itself safe and observable

| Models that now make decisions | Decision Engineering — make the *decision* itself re

只显示了开头部分；其余内容请在仓库中阅读