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Model Council

von Totti0135Version 0.14.0i★ –iLäuft auf deinem Computer
Name in der Registryio.github.Totti0135/model-council

Installieren

Überblick

Beschreibung vom Entwickler (englisches Original)

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

Die drei Säulen von PopNx MCP

Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.

Was du damit machen kannst

Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.

  • Verbindung: Läuft auf deinem Computer
  • Transport: stdio
  • Kategorie: Sonstiges

Installation

Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.

Bevor du installierst

  • Basischecks: 1 von 1 bestanden
  • Läuft auf deinem Computer mit den Rechten deines Benutzerkontos (Dateien, Netzwerk, Apps).
  • Das trägst du selbst ein: COUNCIL_MODELS, COUNCIL_CONFIG

Du brauchst

  • PyPIuv für Python installiert (für den Befehl uvx)

Welche App nutzt du?

Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.

Öffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein

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

Führe das in deinem Terminal aus

claude mcp add model-council --env COUNCIL_MODELS=your-value --env COUNCIL_CONFIG=your-value -- uvx model-council-mcp

Trag die leeren Werte ein, bevor du ihn startest.

In Cursor öffnen

Oder kopier die Einstellungen selbst

In .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen

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

Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.

In VS Code öffnen

Oder kopier die Einstellungen selbst

In .vscode/mcp.json in deinem Projekt einfügen

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

Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.

Dieser Server läuft auf deinem Computer, und ChatGPT kann sich nur mit Servern im Internet verbinden. Wähle eine andere App.

Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.

Werte, die du selbst einträgst

  • 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.

Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.

Was er kann

Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.

Basischecksi

Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 7. Nov. 2026

  • ✓ Bestanden

    Entwickler passt zum Repo

    Der Registry-Name wurde mit dem GitHub-Konto Totti0135 bestätigt, dem das Repo gehört

  • … Noch nicht geprüft

    Wird noch gepflegt

    Dieses Repo haben wir noch nicht von GitHub gelesen

  • … Noch nicht geprüft

    Hat eine Lizenz

    Dieses Repo haben wir noch nicht von GitHub gelesen

  • – Nicht zutreffend

    Endpunkt antwortet

    Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet

  • – Nicht zutreffend

    Anmeldung mit OAuth

    Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen

  • … Noch nicht geprüft

    Keine versteckten Anweisungen in Tool-Beschreibungen

    Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen

Im Vergleich mit anderen in Sonstigesi

  • Mehr Sterne als 0 % der anderen 32661 Server in dieser Kategorie
  • Neuer in der Registry veröffentlicht als 29 % der anderen 32661 Server in dieser Kategorie
  • Basischecks gleich gut wie oder besser als bei 100 % der anderen 32661 Server in dieser Kategorie

Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.

Häufige Fragen

Ist Model Council sicher?

Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.

Welche Schlüssel oder Codes braucht er?

Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.

Läuft er auf meinem Computer oder in der Cloud?

Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).

Wird er noch gepflegt?

Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (0.14.0) kam vor 2 Monaten in die Registry.

Welche Apps können ihn nutzen?

Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Python (uv) auf deinem Computer.

Was kann ich stattdessen nutzen?

In derselben Kategorie: Spec Driven Development, Remote Desktop Commander, Claude Checkpoint MCP

Alternativen

Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.

So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti

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von formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

Läuft auf deinem Computer★ 440MITAktualisiert vor 14 Monaten
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Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

Läuft in der Cloud★ 211Aktualisiert vorgestern
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Claude Checkpoint MCP

von daniel-munoz

MCP server that checkpoints Claude Code session context to survive crashes and token limits

Läuft auf deinem ComputerAktualisiert heute
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README vom Entwickler

README öffnen

Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.

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=

Nur der Anfang wird gezeigt; den Rest findest du im Repo

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