git clone https://github.com/YOUR_USERNAME/multi-model-mcp
cd multi-model-mcp
uv sync

models:
  gpt:
    litellm_model: gpt-4.1
    api_key_env: OPENAI_API_KEY

  claude:
    litellm_model: claude-sonnet-4-5
    api_key_env: ANTHROPIC_API_KEY

  local:
    litellm_model: ollama/qwen3:latest
    api_base: http://localhost:11434   # no key needed

cp .env.example .env
# edit .env with your keys

{
  "mcpServers": {
    "multi-model": {
      "command": "uv",
      "args": [
        "run",
        "--project", "/path/to/multi-model-mcp",
        "multi-model-mcp"
      ],
      "env": {
        "OPENAI_API_KEY": "sk-...",
        "ANTHROPIC_API_KEY": "sk-ant-...",
        "GEMINI_API_KEY": "...",
        "MODELS_CONFIG_PATH": "/path/to/multi-model-mcp/models.yaml"
      }
    }
  }
}

uv tool install .

# Simple query
Use ask_model with alias "gpt" to explain backpressure in streaming systems.

# Parallel comparison
Use ask_many with aliases ["gpt", "claude", "gemini"] to explain the CAP theorem.
Compare their answers.

# Multi-model reasoning
Use reason_together with task "Should we use event sourcing for this service?"
model_aliases ["gpt", "gemini"], critic_model_alias "claude", strategy "independent_then_critique"

# Debate
Use reason_together with task "Is GraphQL worth the complexity over REST?"
model_aliases ["gpt", "claude"], critic_model_alias "gemini", strategy "debate", rounds 2

# Red-team a decision
Use reason_together with task "Our plan is to use a single Postgres instance for all tenants"
model_aliases ["gpt", "gemini", "groq"], strategy "red_team", rounds 2

# Critique a draft
Use critique_answer with question "What is eventual consistency?"
draft_answer "It means data will eventually be the same across nodes."
model_aliases ["claude", "gpt"]

# Pick the best
Use pick_best_answer with question "What is the best way to handle auth tokens?"
candidate_answers ["Store in localStorage", "Store in httpOnly cookies", "Store in memory only"]
judge_model_alias "claude"