Batch Invocation

import concurrent.futures

def batch_invoke(client, mcp, action, token, params_list, max_workers=10):
    """
    Invoke same action with multiple parameter sets in parallel.

    Args:
        client: ArmorIQClient instance
        mcp: MCP name
        action: Action name
        token: Intent token
        params_list: List of parameter dicts
        max_workers: Max concurrent workers

    Returns:
        List of results in same order as params_list
    """
    def invoke_one(params):
        try:
            return client.invoke(mcp, action, token, params)
        except Exception as e:
            return {"success": False, "error": str(e)}

    with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
        futures = [executor.submit(invoke_one, params) for params in params_list]
        return [f.result() for f in futures]

# Usage
captured_plan = client.capture_plan(
    llm="gpt-4",
    prompt="Analyze multiple datasets in parallel"
)
token = client.get_intent_token(captured_plan)["token"]

params_list = [
    {"data": [1, 2, 3], "metrics": ["mean"]},
    {"data": [4, 5, 6], "metrics": ["median"]},
    {"data": [7, 8, 9], "metrics": ["std"]},
    # ... 100 total
]

results = batch_invoke(client, "analytics-mcp", "analyze", token, params_list)