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Model Council

by Totti0135version 0.14.0i★ –iRuns on your computer
Registry nameio.github.Totti0135/model-council

Install

Overview

Description from the maker (original English)

Consult other LLMs as tools — ask them, compare their answers, synthesize a conclusion.

PopNx MCP’s three pillars

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

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: Other

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: COUNCIL_MODELS, COUNCIL_CONFIG

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

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": {
    "model-council": {
      "command": "uvx",
      "args": [
        "model-council-mcp"
      ],
      "env": {
        "COUNCIL_MODELS": "your-value",
        "COUNCIL_CONFIG": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add model-council --env COUNCIL_MODELS=your-value --env COUNCIL_CONFIG=your-value -- uvx model-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": {
    "model-council": {
      "command": "uvx",
      "args": [
        "model-council-mcp"
      ],
      "env": {
        "COUNCIL_MODELS": "your-value",
        "COUNCIL_CONFIG": "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": {
    "model-council": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "model-council-mcp"
      ],
      "env": {
        "COUNCIL_MODELS": "your-value",
        "COUNCIL_CONFIG": "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

  • COUNCIL_MODELS env
    Comma-separated short ids for the models to seat, e.g. 'gpt5,glm'. Each id then takes its own _BASE_URL, _API_KEY, _MODEL and optional _FORMAT (openai or anthropic). Omit to use a config file instead.
  • COUNCIL_CONFIG env
    Path to a JSON config file, for rosters large enough that several models share one endpoint. Defaults to ~/.config/model-council/config.json when that exists.

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 with the GitHub account Totti0135, which owns the repo

  • … 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 Otheri

  • More stars than 0% of the other 32727 servers in this category
  • Published to the registry more recently than 29% of the other 32727 servers in this category
  • Basic checks equal to or better than 100% of the other 32727 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 Model 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?

The registry does not list a required key, though some servers ask you to sign in on first use.

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 (0.14.0) reached the registry 2 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Foliosio

Alternatives

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Runs in the cloud★ 211Updated 2 days ago
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Foliosio

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Read your support inbox, tag conversations and save draft replies from your AI agent. Never sends.

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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.

Model Council

English | 简体中文

An MCP server that seats other LLMs at your table. Your assistant asks them, reads their answers as tool results, relays those answers back and forth for critique, and gives you one merged conclusion — inside a single normal conversation, with no copy-paste.

Your assistant chairs the council. Any number of members, from any mix of OpenAI-compatible and Anthropic-compatible endpoints — a hosted API, a self-run gateway, a local server, or several of each.

Tools

ToolWhat it does
ask(model, prompt, materials?)Ask one member by id
ask_all(prompt, models?, rounds?, guests?, materials?, steelman?)Ask everyone (or a named subset) the same prompt in parallel, answers side by side. rounds=2 turns it into a discussion; steelman seats a standing objection that argues back every round; guests seats answers you already have; materials hands them a file to read
revise(prompt, answers, round?, materials?)Run one more round yourself: show the members everything said last round — including answers only you can produce — and get them back revised
revision_prompt(prompt, answers, seat, materials?)The prompt to hand your own seat for the next round, word-for-word what the members got. No network calls
list_council()The roster: ids, endpoints, weights, what each member can be shown, the route each takes out, call budget, and whether it is ready. No network calls
probe_models(model?)Ask a provider's /models route what ids it really exposes

Members are stateless and cannot see your conversation, so the chair passes everything they need in each call. That is exactly what makes cross-review work: it puts one member's answer inside another's prompt.

Giving the council something to read

A question is usually about something — a spec, a log, a diff, a screenshot. The obvious way to include it is to paste it into prompt, and that is the expensive one, in the place nobody watches: prompt is an argument the chair *writes*, so a long document costs a full copy of itself in generated tokens on every call, and what reaches the members is whatever the chair managed to reproduce. For forty pages that is not reliably the document. A council reviewing a paraphrase is not reviewing the thing, and nothing in the transcript would say so.

materials names it instead:

ask_all(

prompt="Where would this break under load?", materials=[ {"label": "The design", "path": "/abs/path/design.md"}, {"label": "Latency graph", "path": "/abs/path/p99.png"}, {"label": "My summary", "text": "…something with no file behind it…"}, ], rounds=2, )

The server reads each one and puts it ahead of the question, in the same place for every member and every round. The position is not presentation:

  • Identical bytes. Every member argues with the same copy, which is what makes their disagreement about the document rather than about which version each was given.
  • A prefix that can be cached. Everything up to the end of the material is the same in every call of a discussion. The Anthropic format is told so explicitly, with one cache_control breakpoint at that boundary; endpoints that cache by prefix on their own get the shape they need either way. Set "cache": false on a member whose gateway rejects the field.
  • The chair pays once. A path costs the chair a line, not a copy of the document, and ask_all carries the material into round 2 and 3 by itself.

Images are the case that matters most. Without them the chair has to describe the screenshot in prose — and then every member reads the *same* description, so anything the chair misread is misread by the whole council at once, and cross-review cannot recover it. It is the one input where passing it badly quietly removes the independence the council is for. Png, jpeg, gif and webp go to every member whose model can see; a member that cannot should be configured "vision": false, and it then sits out the calls that carry an image rather than answering about text alone as though it had seen the picture. The transcript names who sat out.

How to tell which members those are: a member that cannot see rarely says so. One Anthropic-compatible gateway tested while building this accepted the image blocks, discarded them, and the model then named a colour — not "I was sent no image", which the prompt had explicitly asked for in that case. Text material through the same endpoint arrived intact, so nothing about the call looked wrong. Show a member one unambiguous picture and ask what is in it; a confidently wrong answer is the symptom, and "vision": false is the fix.

Two limits worth knowing. A material that cannot be read stops the call — a council asked about a document it never received will answer anyway, fluently, and read exactly like one that had. And revision_prompt *names* your files rather than pasting them back, at the position the members were given them, so open them for your own seat before you hand it the rest.

Whether this server will read a path at all depends on how it was started; over HTTP it will not, unless its operator named a directory. list_council says which, in a line above the table.

Rounds

ask_all(prompt, rounds=2) runs that cross-review for you. Round 1 is the usual parallel ask. Round 2 goes back to each member carrying the question plus every answer from round 1 — its own and the others', verbatim — and asks it to revise: take what is right, correct what is not, and say where it still disagrees and why. The transcript comes back round by round, so you can see who moved and who held their ground.

Carrying the previous round back is the whole mechanism. Members remember nothing between calls, so without it a second round is just the same question asked twice. Up to 3 rounds; each one costs another call per member and a longer prompt than the last, so 1 is right for a survey of opinion and 2 for a question where the disagreement is the interesting part.

`rounds` is chosen before anything has been asked, which is the one thing wrong with it: you commit to a second round without having read the first, and a council that turns out to agree costs exactly what one still arguing would. Ask with rounds=1 when you would rather look first, then buy the next round with revise — it runs exactly one more, as many times as you judge it worth, and has no ceiling of its own. Reading before you buy is usually the cheaper side: another round is a full call per member, while revise costs you only the answers written back into it. The 3 on ask_all is a ceiling on what one call will spend, not on what the discussion can have, and the transcript says so when you reach it.

Members can also carry different weights, for the common case where the council is not a council of equals.

The standing objection

A council mostly agrees, and its agreement is the least informative thing it produces. Everything above works *against* convergence — the members are anonymous to each other, they are never told the weights, and the closing instruction tells them not to cave — but none of it creates any pressure to diverge. The strongest objection to a plan is not volunteered by members who think the plan is fine.

steelman seats one:

ask_all(prompt, rounds=3, steelman={})

One member writes the strongest case against whatever the table has converged on, each round, and it goes back to everyone as an ordinary anonymous answer. In the next round they have to deal with it.

No member is told to argue a side it does not hold. That is the line, and it is what separates this from a debate mode. What the members say is still what they think; the assignment lives in one extra call they are never told about. Handed a seat marked "arguing against", a model discounts the argument instead of answering it — so the provenance goes to you, in a note at the end of the transcript, and the argument goes to them.

It speaks every round, not once. This is the part that looks like a detail and is not. An objection that cannot reply to its own rebuttal is *quoted* rather than represented: it cannot correct a misreading of itself, so by the third round the table is arguing with its paraphrase and calling that an answer. tenure buys fewer rounds than the default, and when the seat is retired early the transcript says it was retired by configuration — because an unexplained silence reads exactly like a position abandoned.

The mirror of that is worth as much: the last thing the objection says is the last thing in the transcript, so nobody has been asked to take it on, and the transcript says that too. An objection nobody answered is not a point that stood — it is a point that was never examined. With rounds=2 that is true of everything it said, which is why the note names the setting that fixes it rather than leaving you to notice after paying.

Read what it produces as the strongest objection this council can make to order, never as evidence that anyone holds it. A point of its that survives being answered is worth something; the same point in the round it appeared is worth nothing yet.

By default the first member being asked writes it, and also answers as itself — the two calls are unrelated as far as it knows. Name another with steelman={"model": "glm"}.

Seating an answer you already have

Your assistant is not only the chair — it can answer too, and in Claude Code or Codex it can spawn a subagent to answer as well. Those answers used to sit *beside* the council's, compared by hand at the end. guests puts them *in* it:

ask_all(

prompt="What are the traps in this plan?", guests=[{"label": "Subagent", "text": " "}], rounds=2, )

Round 1 prints it beside the members. From round 2 every member is handed the text verbatim and asked to argue with it:

--- ANSWER C (does not revise between rounds) ---

the migration has no rollback path

That is the whole difference. Without it the models never learn your subagent had an opinion, and you are left doing the comparison yourself — which is the work the rounds mechanism already does better, because it lets the models answer each other rather than answering into a void.

Notes worth having:

  • Pass the text verbatim, not a summary. The members are shown exactly what you pass, and a summary is not the thing you wanted critiqued.
  • A guest speaks once. There is nobody to ask it for a revision, so it does not reappear after round 1. The transcript says so, because a seat that vanishes otherwise reads as a position abandoned.
  • A guest counts as a voice. One member plus one guest is enough for a real second round — your subagent against one model is still a discussion.
  • `weight` works on guests too, on the same scale as the members'.
  • Guests are per call. Nothing is configured, and nothing persists.
A subagent as a full member

A guest speaks once. To let a voice you produce yourself *keep up* with the members — answer, read the others, revise, round after round, exactly as they do — you have to drive the rounds, because this server cannot spawn your subagent. revise runs one round on demand:

ask_all(prompt)                                  → members answer round 1

...you spawn your subagent on the same prompt → its round-1 answer

revise(prompt, round=1, answers=[ {"model": "glm", "text": " "}, {"model": "sol", "text": " "}, {"label": "Subagent", "text": " "}, ]) → members come back revised ...you re-run your subagent on the same material

revise(prompt, round=

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