Tokonomix Council

Name: Tokonomix Council
Author: TokonoMix

by TokonoMixversion 1.14.0i★ –iRuns on your computer

Registry nameai.tokonomix/council

Install

Overview

Description from the maker (original English)

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

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory
You need to fill in: TOKONOMIX_API_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: TOKONOMIX_API_KEY

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

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

Run this in your terminal

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

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

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

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified against the domain tokonomix.ai (the registry checks domain ownership)
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 5653 servers in this category
Published to the registry more recently than 51% of the other 5653 servers in this category
Basic checks equal to or better than 100% of the other 5653 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Tokonomix Council safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: TOKONOMIX_API_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.14.0) reached the registry 40 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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Codebase Memory

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Exa

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

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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

Only the beginning is shown; read the rest in the repo

Tokonomix Council

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

tokonomix-council-mcp

The Tokonomix ecosystem (where Council fits)

Status — what is live right now

What Council owns — and what it explicitly does not

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

Contents

Why Decision Engineering is becoming necessary