Model Council

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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

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.

Install

The server runs from PyPI with no clone and no virtualenv. You need uv:

curl -LsSf https://astral.sh/uv/install.sh | sh

Claude Desktop

Edit claude_desktop_config.json (Settings → Developer → Edit Config), add the block below, then fully quit and reopen the app — Cmd-Q, not just closing the window. You will know it worked when the tools menu lists model-council.

{
  "mcpServers": {
    "model-council": {
      "command": "uvx",
      "args": ["model-council-mcp"],
      "env": {
        "COUNCIL_MODELS": "gpt5,glm",
        "GPT5_BASE_URL": "https://your-openai-compatible-host/v1",
        "GPT5_API_KEY": "sk-xxxxxxxx",
        "GPT5_MODEL": "gpt-5",
        "GLM_BASE_URL": "https://open.bigmodel.cn/api/anthropic",
        "GLM_API_KEY": "xxxxxxxx",
        "GLM_MODEL": "glm-4.6",
        "GLM_FORMAT": "anthropic"
      }
    }
  }
}

Claude Code

claude mcp add model-council -e GPT5_BASE_URL=... -e GPT5_API_KEY=... -- uvx model-council-mcp

Other MCP clients

Anything that launches a stdio server works: run uvx model-council-mcp and pass the same environment variables.

Configuring the council

Two layers, so several models can share one endpoint without repeating its credentials:

• provider — an endpoint: base_url + api_key + which wire format it speaks
• member — one model on some provider, addressed by a short id

Configuration comes from a JSON file if one is found, otherwise from environment variables. list_council() always reports which source won.

Environment variables

COUNCIL_MODELS lists the ids; each id gets variables named after it, uppercased with non-alphanumeric characters turned into underscores (my-model → MY_MODEL_BASE_URL).

COUNCIL_MODELS=gpt5,glm
GPT5_BASE_URL=https://your-openai-compatible-host/v1
GPT5_API_KEY=sk-xxxxxxxx
GPT5_MODEL=gpt-5
GLM_BASE_URL=https://open.bigmodel.cn/api/anthropic
GLM_API_KEY=xxxxxxxx
GLM_MODEL=glm-4.6
GLM_FORMAT=anthropic

Per member: _BASE_URL, _API_KEY, _MODEL, _FORMAT, _LABEL, _MAX_TOKENS, _TEMPERATURE, _TIMEOUT, _HEADERS (a JSON object), _ENABLED. Globally: COUNCIL_TIMEOUT, COUNCIL_CONFIG, COUNCIL_ENV_FILE.

Omit COUNCIL_MODELS and the roster defaults to chatgpt,glm, reading CHATGPT_* and GLM_*.

A config file

Better once you have more than a handful of members, or when several share an endpoint. Set COUNCIL_CONFIG=/path/to/config.json, or drop the file at ~/.config/model-council/config.json where the server finds it on its own.

{
  "providers": {
    "my-relay": {
      "base_url": "https://your-openai-compatible-host/v1",
      "api_key": "${MY_RELAY_KEY}",
      "format": "openai"
    },
    "zhipu": {
      "base_url": "https://open.bigmodel.cn/api/anthropic",
      "api_key": "${GLM_KEY}",
      "format": "anthropic"
    }
  },
  "members": [
    { "id": "gpt5",  "provider": "my-relay", "model": "gpt-5", "label": "GPT-5" },
    { "id": "codex", "provider": "my-relay", "model": "gpt-5-codex", "temperature": 0.2 },
    { "id": "glm",   "provider": "zhipu",    "model": "glm-4.6" },
    { "id": "kimi",  "base_url": "https://api.moonshot.cn/v1",
      "api_key": "${KIMI_KEY}", "model": "kimi-k2" }
  ]
}

${ENV_VAR} is expanded from the environment, so the file carries no secrets and can be shared or committed. A member may skip provider and inline its own connection details, and any provider field can be overridden per member. See examples/config.json for a fully annotated version.

Fields

Wire format notes

• format is not inferred from the URL. Pointing base_url at an Anthropic-style endpoint without also setting format: "anthropic" leaves the member on the OpenAI format, and every call fails. This is the single most common misconfiguration.
• Anthropic endpoints: the server posts to {base_url}/v1/messages, so base_url should not already include the /v1.
• OpenAI-compatible endpoints: the server uses /chat/completions, never /responses. Some gateways expose both, but /responses may inject a provider-chosen system persona, which is wrong for a general-purpose advisor.
• Model ids move fast. Run probe_models to see what an endpoint actually offers today.

Using it

Things worth typing to the chair:

• "Answer this yourself, then ask_all and give me a table of where you all agree and disagree."
• "Ask gpt5 and glm this, then critique both answers and tell me which is more correct and why."
• "Round 1: ask_all. Round 2: show each member the others' answers and ask it to revise. Then give me the merged answer."
• "Ask only glm — I want a second opinion on this one file."

Local development

uv sync

Copy .env.example to .env, fill in real values, then:

uv run python tests/test_smoke.py

The smoke test checks both configuration paths offline; with a usable .env it finishes with a live round-trip. To point a client at your working copy, use the model-council-mcp script inside your environment instead of uvx.

Troubleshooting

• Server doesn't appear — check the client's MCP logs (Claude Desktop: ~/Library/Logs/Claude/mcp*.log). The server writes configuration warnings to stderr at startup.
• A tool answers [... is not configured] — that member is missing base_url, api_key, or model. Run list_council for a per-member breakdown.
• HTTP 401 — wrong key, or a key the provider has disabled.
• HTTP 404 — wrong base_url, or the wrong format for that endpoint.
• The model id is rejected — run probe_models.

License

MIT