model-council-mcp

An MCP server that routes a question to a council of AI models — local (Ollama, vLLM, TRT-LLM, SGLang) and cloud (OpenAI, Anthropic, Groq) — and synthesizes their answers in three configurable modes:

Mode	What you get
individual	Each model's raw answer, side by side
categorized	Judge groups responses into common agreement, complementary insights, and conflicting positions
deconflicted	Iterative loop — judge re-questions the council on each conflict until resolved or rounds exhausted; returns a deconfliction score (0–100 %)

---

Install as a Claude Code plugin (recommended)

This repo is a self-contained Claude Code plugin — the server is bundled into a single zero-dependency file (bundle/server.cjs), so it runs offline against local models with no npm install step.

# 1. Add this repo as a marketplace (from GitHub)
/plugin marketplace add tsarihan/model-council-mcp

# 2. Install the plugin
/plugin install model-council@model-council

Zero-config: accept the defaults and it just works. With no council models pinned, the council auto-uses every local Ollama model plus your Ollama :cloud models (embedding models like bge-m3 are skipped). The judge auto-selects your largest model. Ask a question immediately — no setup.

On install, Claude Code prompts you for the (all-optional) configurable options — Ollama address, whether to pin specific council models, API keys, default mode, deconfliction rounds. Nothing is required. API keys are stored in your system keychain. Change settings any time from /plugin → Configure.

Local development / test install:

# Validate the manifest
claude plugin validate .

# Load without installing (dev loop)
claude --plugin-dir /path/to/model-council-mcp

Configurable options (prompted at install)

Option	Purpose	Default
Ollama address	Base URL of your Ollama server	http://localhost:11434
Council models	Pin specific models, or leave blank to auto-use all Ollama models	(empty → auto)
Auto-discover council	Use all Ollama chat models (local + :cloud) when none pinned	true
Judge model	Categorizer/deconflicter, or auto (largest)	auto
Default response mode	individual / categorized / deconflicted	categorized
Max deconfliction rounds	1–10	3
OpenAI / Anthropic / Groq API key	Enable cloud models (stored in keychain)	—
vLLM / TRT-LLM / SGLang servers	name:host:port entries	—
Max response tokens	Tokens per completion	16000
Cloud / local concurrency	Simultaneous requests (cloud pool / local pool)	3 / 1
Completion retries	Retries on an empty/failed response	3
Verbose deconfliction	Include per-round detail in deconflicted results	false
Claude subscription (CLI)	Use your Claude Pro/Max subscription via claude -p (no API key)	false

---

Install as a standalone MCP server (npm)

# Quick try
npx model-council-mcp

# Add to Claude Code
claude mcp add model-council-mcp -s user -- npx -y model-council-mcp

Or add to ~/.claude.json → mcpServers:

{
  "mcpServers": {
    "model-council": {
      "command": "npx",
      "args": ["-y", "model-council-mcp"],
      "env": {
        "OLLAMA_ADDRESS": "http://localhost:11434",
        "COUNCIL_MODELS": "ollama:llama3,ollama:mistral",
        "RESPONSE_MODE": "categorized"
      }
    }
  }
}

---

Configuration (environment variables)

Provider connections

Variable	Description	Default
OLLAMA_ADDRESS	Ollama server URL	http://localhost:11434
OPENAI_API_KEY	Enables OpenAI models	—
ANTHROPIC_API_KEY	Enables Anthropic Claude models	—
GROQ_API_KEY	Enables Groq models	—
VLLM_SERVERS	vLLM servers (see below)	—
TRTLLM_SERVERS	TRT-LLM servers	—
SGLANG_SERVERS	SGLang servers	—
CLAUDE_CLI	true → add subscription-backed Claude members via the local claude CLI (no API key)	false
CLAUDE_CLI_MODELS	Model aliases for the CLI member	opus,sonnet
CLAUDE_CLI_PATH	Path to the claude binary	claude

Claude via your subscription (first-party CLI)

Set CLAUDE_CLI=true to add council members that run through the locally-installed Claude Code CLI (claude -p) instead of the Anthropic API. Inference runs under whatever your claude CLI is logged in with — typically your own Claude Pro/Max subscription — so these members don't consume API credits. They appear as claude-cli:opus, claude-cli:sonnet, etc.

Behavior & requirements

• The claude CLI must be installed and logged in (claude → /login, or claude setup-token). Set CLAUDE_CLI_PATH if it isn't on PATH.
• Each call shells out to claude -p with all tools disabled (--tools ""), MCP disabled (--strict-mcp-config, so it can't recurse into this plugin), and sessions not persisted — a clean single text answer.
• ANTHROPIC_API_KEY / ANTHROPIC_AUTH_TOKEN are stripped from the nested call, because the CLI silently prefers an API key over the subscription. So these members stay subscription-billed even if you also set an API key for the regular anthropic: provider.
• They are not auto-discovered — add them explicitly via configure_council or COUNCIL_MODELS (e.g. claude-cli:opus), so they don't quietly draw down your subscription.

Where it works: anywhere the claude CLI actually executes — the Claude Code CLI, or the Claude Desktop app on a machine that also has the CLI. With /remote-control on your CLI, driving it from the Claude web/mobile code tab still runs claude -p on your machine, so it works there too. It does not work for a remotely-hosted copy of this server (no local CLI), and it can't borrow the Claude app's subscription directly (no client supports MCP sampling yet).

This uses the sanctioned first-party CLI under your own subscription, for your own use. High-volume automated fan-out can hit your subscription's rate limits — keep CLOUD_CONCURRENCY modest (these members use the cloud pool). Reusing a subscription token against the raw Anthropic API from a third-party app is a separate thing and is prohibited; this feature does not do that.

OpenAI-compatible server format

Comma-separated list of name:host:port entries.
You can run multiple servers on different ports (e.g. different models on the same GPU host):

VLLM_SERVERS=gpu1:192.168.1.10:8000,gpu2:192.168.1.10:8001
TRTLLM_SERVERS=trt-main:192.168.1.20:8000
SGLANG_SERVERS=sgl1:192.168.1.30:30000

Full URLs also work: gpu3:http://10.0.0.5:9000

Default ports: vLLM → 8000, TRT-LLM → 8000, SGLang → 30000

Council defaults

Variable	Description	Default
COUNCIL_MODELS	Comma-separated model IDs	(empty — use configure_council)
JUDGE_MODEL	Judge model ID or auto	auto (largest council member)
RESPONSE_MODE	individual | categorized | deconflicted	categorized
MAX_DECONFLICT_ROUNDS	Max deconfliction iterations	3

Performance & output

Variable	Description	Default
MAX_TOKENS	Max tokens requested per model completion	16000
CLOUD_CONCURRENCY	Max simultaneous requests to cloud members (Ollama cloud :cloud/-cloud, OpenAI, Anthropic, Groq). Ollama cloud needs Pro (3 concurrent) or Max (10)	3
LOCAL_CONCURRENCY	Max simultaneous requests to local models; 1 runs them one at a time to avoid contention, 0 = unlimited	1
COMPLETION_RETRIES	Attempts per completion before giving up on an empty/failed response	3
DECONFLICT_VERBOSE	true → deconflicted results include per-round detail by default	false

The council queries members in parallel but respects these concurrency limits — cloud members share one pool and local members another, so a large council never exceeds your Ollama cloud plan's concurrent-request cap, and local models can be run sequentially to avoid GPU contention.

Model ID format

provider:model
provider/serverId:model      ← for named multi-server setups

Examples:

ollama:llama3
ollama:mistral:7b-instruct-q4_K_M
openai:gpt-4o
openai:o1-mini
anthropic:claude-opus-4-5
groq:llama-3.3-70b-versatile
vllm/gpu1:meta-llama/Meta-Llama-3-8B-Instruct
trtllm/trt-main:mistralai/Mistral-7B-v0.1
sglang/sgl1:deepseek-ai/DeepSeek-R1

---

MCP Tools

list_models

Discover all models across every configured provider.

{ "filter_provider": "ollama" }

Returns model IDs, parameter size, family, disk size — everything you need to fill configure_council.

---

configure_council

Update the council at runtime (changes persist for the session).

{
  "models": ["ollama:llama3", "ollama:mistral", "openai:gpt-4o"],
  "judge_model": "openai:gpt-4o",
  "response_mode": "deconflicted",
  "max_deconflict_rounds": 4
}

All fields are optional — only supplied fields are updated.

---

ask_council

Send a question to the full council.

{
  "question": "What is the best way to handle errors in a distributed system?",
  "mode": "deconflicted",
  "max_deconflict_rounds": 3
}

mode and max_deconflict_rounds override the configured defaults for this call only. In deconflicted mode, set "verbose": true to include the initial categorization, every member's per-round responses, and the round-by-round re-categorization alongside the final synthesis.

Individual result

{
  "mode": "individual",
  "question": "...",
  "responses": [
    { "label": "ollama:llama3", "response": "...", "latencyMs": 1240 },
    { "label": "openai:gpt-4o", "response": "...", "latencyMs": 843 }
  ]
}

Categorized result

{
  "mode": "categorized",
  "question": "...",
  "commonAgreement": "All models agree that ...",
  "complementary": [
    { "aspect": "performance", "models": ["ollama:llama3"], "insight": "..." }
  ],
  "conflicting": [
    {
      "id": "conflict-1",
      "topic": "retry strategy",
      "positions": [
        { "models": ["ollama:llama3"], "position": "exponential backoff" },
        { "models": ["openai:gpt-4o"], "position": "circuit breaker preferred" }
      ]
    }
  ],
  "judgeModel": "openai:gpt-4o"
}

Deconflicted result

{
  "mode": "deconflicted",
  "question": "...",
  "roundsTaken": 2,
  "maxRounds": 3,
  "deconflictionScore": 75,
  "resolved": 3,
  "totalConflicts": 4,
  "finalSynthesis": "The council recommends ...",
  "unresolvedConflicts": [ { "id": "conflict-3", "topic": "...", "positions": [...] } ],
  "roundHistory": [
    { "round": 1, "conflictsEntering": 4, "conflictsResolved": 2, "conflictsRemaining": 2 },
    { "round": 2, "conflictsEntering": 2, "conflictsResolved": 1, "conflictsRemaining": 1 }
  ],
  "judgeModel": "openai:gpt-4o"
}

Deconfliction score: resolved / totalConflicts × 100.
100 % means all conflicts resolved; n/m means n conflicts resolved out of m found.

---

get_council_config

Returns current council settings plus all configured provider connections and the full env-var reference.

---

Deconfliction algorithm

1. Query all council members in parallel → N raw responses
2. Judge model categorises → common / complementary / M conflicts
3. If M = 0 → synthesise final answer, score = 100 %
4. For each round r in 1..maxRounds:
   a. Ask all members specifically about each open conflict
   b. Judge re-categorises conflict responses
   c. Conflicts where positions converge → marked resolved
   d. If no conflicts remain → break
5. Score = resolvedCount / M × 100
6. Judge synthesises final answer, noting any unresolved conflicts

---

Example: full multi-provider setup

{
  "mcpServers": {
    "model-council": {
      "command": "npx",
      "args": ["-y", "model-council-mcp"],
      "env": {
        "OLLAMA_ADDRESS": "http://localhost:11434",
        "OPENAI_API_KEY": "sk-...",
        "ANTHROPIC_API_KEY": "sk-ant-...",
        "GROQ_API_KEY": "gsk_...",
        "VLLM_SERVERS": "gpu1:192.168.1.10:8000,gpu2:192.168.1.10:8001",
        "SGLANG_SERVERS": "sgl1:192.168.1.30:30000",
        "COUNCIL_MODELS": "ollama:llama3,ollama:mistral,openai:gpt-4o,anthropic:claude-sonnet-4-5,groq:llama-3.3-70b-versatile",
        "JUDGE_MODEL": "anthropic:claude-opus-4-5",
        "RESPONSE_MODE": "deconflicted",
        "MAX_DECONFLICT_ROUNDS": "3"
      }
    }
  }
}

---

Background

The council's value comes from decorrelation: routing a question to independent models from different families and providers surfaces systematic biases and blind spots that any single model — or a set of correlated ones — would hide. The categorized and deconflicted modes make that disagreement explicit and then work to resolve it.

This design is informed by The Mirror Law, which shows that a learner trained against a single reference reproduces that reference's error field — so the bias is invisible from the loss curve alone, and a decorrelated second reference is what makes the hidden bias observable.

Sarihan, Tom. The Mirror Law: Reference Quality and the Transfer of Systematic Bias in Imitation and Distillation. Preprint, 2026. DOI: 10.5281/zenodo.21282027. Code and materials: github.com/tsarihan/MirrorLaw.

@article{sarihan2026mirror,
  title  = {The Mirror Law: Reference Quality and the Transfer of Systematic Bias in Imitation and Distillation},
  author = {Sarihan, Tom},
  year   = {2026},
  doi    = {10.5281/zenodo.21282027},
  note   = {Preprint}
}

---

License

Apache License 2.0 — Copyright (c) 2026 Tom Sarihan. See LICENSE and NOTICE.