Apply Agent SDK Hooks for Tool Call Interception & Data Normalization

from anthropic_agent_sdk import AgentHook, ToolResultContext
import datetime

# Status code mapping for legacy order management system
ORDER_STATUS_MAP = {
    0: "pending",
    1: "confirmed",
    2: "shipped",
    3: "delivered",
    4: "cancelled",
    5: "refunded"
}

class DataNormalizationHook(AgentHook):
    """PostToolUse hook — normalizes heterogeneous data from multiple MCP tools."""

    async def post_tool_use(self, ctx: ToolResultContext):
        tool_name = ctx.tool_name
        result = ctx.result  # Raw tool output (dict)

        # ── Normalize lookup_order results ──────────────────────────────
        if tool_name == "lookup_order":
            # 1. Convert Unix timestamp → ISO 8601
            if "created_at" in result and isinstance(result["created_at"], int):
                result["created_at"] = datetime.datetime.utcfromtimestamp(
                    result["created_at"]
                ).isoformat() + "Z"

            # 2. Translate numeric status code → human-readable string
            if "status" in result and isinstance(result["status"], int):
                result["status"] = ORDER_STATUS_MAP.get(
                    result["status"], f"unknown_status_{result['status']}"
                )

        # ── Normalize get_customer results ──────────────────────────────
        if tool_name == "get_customer":
            # Normalize "April 1, 2024" or "01/04/2024" → "2024-04-01"
            if "member_since" in result:
                result["member_since"] = _normalize_date(result["member_since"])

        # Return normalized result — Claude will now reason on consistent data
        return ctx.with_result(result)


def _normalize_date(raw: str) -> str:
    """Attempt to parse multiple date formats and return ISO 8601."""
    for fmt in ("%B %d, %Y", "%d/%m/%Y", "%Y-%m-%d"):
        try:
            return datetime.datetime.strptime(raw, fmt).strftime("%Y-%m-%d")
        except ValueError:
            continue
    return raw  # Return original if unparseable

from anthropic_agent_sdk import AgentHook, ToolCallContext

# Business policy constants — single source of truth
REFUND_AUTO_APPROVAL_LIMIT = 500.00  # USD
BULK_CANCEL_ITEM_LIMIT = 10

class ComplianceEnforcementHook(AgentHook):
    """PreToolCall hook — enforces business rules before any tool executes."""

    async def pre_tool_call(self, ctx: ToolCallContext):
        tool_name = ctx.tool_name
        tool_input = ctx.tool_input  # The parameters Claude wants to pass

        # ── Rule 1: Refunds above threshold require human approval ──────
        if tool_name == "process_refund":
            amount = tool_input.get("amount", 0)

            if amount > REFUND_AUTO_APPROVAL_LIMIT:
                # Block the refund and tell Claude WHY + what to do instead
                return ctx.block(
                    error=f"POLICY_VIOLATION: Refund of ${amount:.2f} exceeds the "
                          f"${REFUND_AUTO_APPROVAL_LIMIT:.2f} auto-approval limit. "
                          f"Use escalate_to_human with reason='large_refund' and "
                          f"include: customer_id, refund_amount={amount}, "
                          f"root_cause, and recommended_action in the handoff."
                )

        # ── Rule 2: Bulk cancellations above limit require human review ─
        if tool_name == "cancel_orders":
            order_ids = tool_input.get("order_ids", [])

            if len(order_ids) > BULK_CANCEL_ITEM_LIMIT:
                return ctx.block(
                    error=f"POLICY_VIOLATION: Bulk cancellation of {len(order_ids)} orders "
                          f"exceeds the {BULK_CANCEL_ITEM_LIMIT} order limit for automated "
                          f"processing. Escalate to human for review."
                )

        # ── Allow all other tool calls to proceed unmodified ────────────
        return ctx.allow()