Spec Driven Development
제작자: formulahendry
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
내 컴퓨터에서 실행★ 440MIT14개월 전 업데이트
설치
Tokonomix Council
개요
제작자가 쓴 설명(영어 원문)
Multi-model consensus: 2-6 frontier LLMs answer, an independent judge synthesises one answer.
PopNx MCP의 세 기둥
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
- 사용자 의견i아직 리뷰가 없어요리뷰는 다음 단계에서 계정 기능과 함께 열려요
- 우리의 기본 점검i1개 중 1개 통과공개된 정보로 점검했으며, 코드는 아직 스캔하지 않았어요
- 사용 정도i아직 측정 안 함★ –
할 수 있는 일
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
- 연결 방식: 내 컴퓨터에서 실행
- 전송 방식: stdio
- 카테고리: 기타
- 입력해야 할 값: TOKONOMIX_API_KEY
설치
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
설치하기 전에
- 기본 점검: 1개 중 1개 통과
- 내 컴퓨터에서 내 사용자 계정의 권한(파일, 네트워크, 앱)으로 실행돼요.
- 직접 입력할 값:
TOKONOMIX_API_KEY
비밀로 표시된 값은 내 계정의 열쇠예요. 절대 다른 사람과 공유하거나 채팅에 붙여 넣지 마세요.
필요한 것
- npmNode.js 설치(npx 명령어용)
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"council": {
"command": "npx",
"args": [
"-y",
"tokonomix-council-mcp"
],
"env": {
"TOKONOMIX_API_KEY": "your-value"
}
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add council --env TOKONOMIX_API_KEY=your-value -- npx -y tokonomix-council-mcp실행하기 전에 빈 값을 채워 주세요.
또는 설정을 직접 복사하기
프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"council": {
"command": "npx",
"args": [
"-y",
"tokonomix-council-mcp"
],
"env": {
"TOKONOMIX_API_KEY": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
또는 설정을 직접 복사하기
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"council": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"tokonomix-council-mcp"
],
"env": {
"TOKONOMIX_API_KEY": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
직접 입력할 값
- Tokonomix API key (tok_live_...). Optional - keyless onboarding works without it.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
할 수 있는 일
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
기본 점검i
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 7일 점검
- ✓ 통과
제작자와 저장소 일치
레지스트리 이름이 도메인 tokonomix.ai 기준으로 확인되었어요(레지스트리가 도메인 소유를 확인해요)
- … 아직 점검 안 함
계속 관리되고 있음
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
- … 아직 점검 안 함
라이선스 있음
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
- – 해당 없음
엔드포인트 응답
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
- – 해당 없음
OAuth로 로그인
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
- … 아직 점검 안 함
도구 설명에 숨겨진 지시 없음
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
기타 카테고리의 다른 서버와 비교i
- 이 카테고리의 다른 서버 32686개 중 0%보다 스타가 많아요
- 이 카테고리의 다른 서버 32686개 중 51%보다 최근에 레지스트리에 공개됐어요
- 기본 점검 결과가 이 카테고리의 다른 서버 32686개 중 100%와 같거나 더 좋아요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
자주 묻는 질문
Tokonomix Council, 안전한가요?
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
어떤 키나 코드가 필요한가요?
입력해야 할 값: TOKONOMIX_API_KEY
내 컴퓨터에서 실행되나요, 클라우드에서 실행되나요?
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
아직 관리되고 있나요?
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(1.14.0)은 40일 전 레지스트리에 올라왔어요.
어떤 앱에서 쓸 수 있나요?
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Node.js도 있어야 해요.
대신 쓸 수 있는 건 뭐가 있나요?
같은 카테고리에는: Spec Driven Development, Remote Desktop Commander, Microsoft Exchange Online MCP
대안
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
Remote Desktop Commander
제작자: desktop-commander
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
클라우드에서 실행★ 211그저께 업데이트
설치
Microsoft Exchange Online MCP
제작자: servosity
Exchange Online admin for MSPs: forwarding audits, EWS posture and offboarding, no PowerShell
내 컴퓨터에서 실행오늘 업데이트
설치
제작자의 README
README 열기
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
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
앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요