Spec Driven Development
作者：formulahendry
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
パソコンで動く★ 440MIT14 か月前更新
インストール
Tokonomix Council
概要
作者による説明（英語の原文）
Multi-model consensus: 2-6 frontier LLMs answer, an independent judge synthesises one answer.
PopNx MCPの3つの柱
この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。
- 利用者の声iレビューはまだありませんレビューは今後のステップで、アカウント機能と一緒に始まります
- 私たちの基本チェックi1項目中1項目に合格公開情報からチェック。コードはまだスキャンしていません
- 利用状況i未計測★ –
できること
このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。
- 接続方法: パソコンで動く
- トランスポート: stdio
- カテゴリ: その他
- 入力が必要なもの：TOKONOMIX_API_KEY
インストール
使っているAIアプリを選んで、設定をコピーしてください。
インストールの前に
- 基本チェック：1項目中1項目に合格
- あなたのパソコンで、あなたのユーザーと同じ権限（ファイル、ネットワーク、アプリ）で動きます。
- 自分で入力するもの：
TOKONOMIX_API_KEY
「秘密」と表示された値は、あなたのアカウントの鍵です。絶対に人に教えたり、チャットに貼り付けたりしないでください。
必要なもの
- npmNode.jsのインストール（npxコマンドに必要）
どのアプリを使っていますか？
上でアプリを選んでください。選んだアプリを覚えておくので、次からはボタンに「…にインストール」と表示されます。
Claude Desktop → Settings → Developer → Edit Config を開き、claude_desktop_config.json にこれを貼り付けます
{
"mcpServers": {
"council": {
"command": "npx",
"args": [
"-y",
"tokonomix-council-mcp"
],
"env": {
"TOKONOMIX_API_KEY": "your-value"
}
}
}
}ターミナルでこれを実行します
claude mcp add council --env TOKONOMIX_API_KEY=your-value -- npx -y tokonomix-council-mcp動かす前に、空欄の値を入力してください。
または、設定を自分でコピーする
プロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"mcpServers": {
"council": {
"command": "npx",
"args": [
"-y",
"tokonomix-council-mcp"
],
"env": {
"TOKONOMIX_API_KEY": "your-value"
}
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
または、設定を自分でコピーする
プロジェクトの .vscode/mcp.json に貼り付けます
{
"servers": {
"council": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"tokonomix-council-mcp"
],
"env": {
"TOKONOMIX_API_KEY": "your-value"
}
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
これはパソコンで動くサーバーですが、ChatGPTがつなげるのはインターネット上のサーバーだけです。ほかのアプリを選んでください。
選んだアプリは、この端末のCookieに保存されます。アカウントは不要です。
自分で入力する値
- Tokonomix API key (tok_live_...). Optional - keyless onboarding works without it.
この設定は、作者のレジストリ登録情報から作成しています。READMEと違う場合は、READMEに従ってください。
できること
パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。
基本チェックi
誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月7日にチェック
- ✓ 合格
作者とリポジトリが一致
レジストリ名はドメイン tokonomix.ai で確認済みです（レジストリがドメインの所有を確認しています）
- … 未チェック
今もメンテナンスされている
このリポジトリはまだGitHubから読み取っていません
- … 未チェック
ライセンスがある
このリポジトリはまだGitHubから読み取っていません
- – 対象外
エンドポイントが応答する
これはパソコンで動くため、インターネット上のエンドポイントはありません
- – 対象外
OAuthでサインイン
パソコンで動き、代わりにローカルの設定を使います
- … 未チェック
ツールの説明に隠れた指示がない
パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです
その他のほかのサーバーとの比較i
- このカテゴリのほかの32706件のうち0%よりスターが多い
- このカテゴリのほかの32706件のうち50%より最近レジストリに公開
- 基本チェックの結果が、このカテゴリのほかの32706件のうち100%と同じかそれ以上
レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。
よくある質問
Tokonomix Councilは安全ですか？
当てはまる基本チェック1項目のうち1項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。
どんなキーやコードが必要ですか？
入力が必要なもの：TOKONOMIX_API_KEY
パソコンで動きますか、それともクラウドですか？
あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません（サーバー自体がオンラインサービスを呼び出す場合を除きます）。
今もメンテナンスされていますか？
GitHubリポジトリの最終更新は–、最新バージョン（1.14.0）がレジストリに届いたのは40 日前です。
どのアプリで使えますか？
Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。パソコンにNode.jsも必要です。
代わりに使えるものは？
同じカテゴリには：Spec Driven Development, Remote Desktop Commander, Kanal Varna — drains, leaks & plumbing (Varna, BG)
代わりの候補
同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。
カードの見方：★ GitHubスターi3つの柱：レビュー · チェック · 利用状況i更新＝最新バージョンをレジストリに公開した日i
Remote Desktop Commander
作者：desktop-commander
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
クラウドで動く★ 211一昨日更新
インストール
Kanal Varna — drains, leaks & plumbing (Varna, BG)
作者：kanalvarna.com
Drain unblocking, leak detection & plumbing in Varna, Bulgaria: services, prices, areas, contact
クラウドで動く今日更新
インストール
作者のREADME
READMEを開く
作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。
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.
Contents
- 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
冒頭だけを表示しています。続きはリポジトリで読めます