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.

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), 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.

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