原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

Model Council

作者:Totti0135版本 0.14.0i★ –i在你的电脑上运行
注册名称io.github.Totti0135/model-council

安装

概览

作者提供的说明(英文原文)

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

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 其他

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 需要你自己填写: COUNCIL_MODELS, COUNCIL_CONFIG

你需要

  • PyPI已安装 Python 的 uv(用于 uvx 命令)

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json

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

在终端中运行这条命令

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

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

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

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

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

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上,无需账户。

需要你填写的值

  • 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.

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。

它能做什么

它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 Totti0135 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    它在你的电脑上运行;我们只能读取互联网端点的工具列表

与“其他”中的其他服务器比较i

  • 星标数多于此分类中其他 32744 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 32744 个服务器的 29% 更新
  • 基础检查结果等于或优于此分类中其他 32744 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。

常见问题

Model Council 安全吗?

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端?

它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(0.14.0)于2个月前进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。

有什么可以替代它?

同一分类中有:Spec Driven Development, Remote Desktop Commander, Date Math

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Spec Driven Development

作者:formulahendry

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

在你的电脑上运行★ 440MIT14个月前更新
安装

Remote Desktop Commander

作者:desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

在云端运行★ 211前天更新
安装

Date Math

作者:JowenDelaPena

Exact date/time math for agents: business days, holidays, time zones, cron, RRULE, SLA. x402 paid.

在云端运行今天更新
安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

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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