Tokonomix Council MCP server

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.

Shortened. The full README is on GitHub.