import * as z from "zod/v4";
export function registerCostAnomalyPrompt(server) {
    server.registerPrompt("cost-anomaly-investigation", {
        description: "Investigate cost anomalies — determine root cause, impacted resources, and remediation",
        argsSchema: {
            projectId: z.string().describe("Project identifier").optional(),
        },
    }, async ({ projectId }) => {
        const projectFilter = projectId ? `, project_id="${projectId}"` : "";
        return {
            messages: [{
                    role: "user",
                    content: {
                        type: "text",
                        text: `Investigate recent cost anomalies and determine root causes.

Steps:
1. **Fetch anomalies**: Call harness_list with resource_type="cost_anomaly"${projectFilter} to get recent cost anomalies
2. **Get cost timeline**: Call harness_get with resource_type="cost_timeseries"${projectFilter} to see the cost spike in context
3. **Breakdown analysis**: Call harness_get with resource_type="cost_breakdown"${projectFilter} to identify which services/resources drove the spike
4. **Check ignored**: Call harness_list with resource_type="cost_anomaly"${projectFilter}, filters={status: "IGNORED"} to see if similar anomalies were previously dismissed
5. **For each anomaly, provide**:
   - **When**: Date and time of the anomaly
   - **What**: Which resource/service saw the cost spike
   - **How much**: Dollar impact (expected vs actual)
   - **Root cause**: Most likely explanation (autoscaling event, new deployment, misconfiguration, attack)
   - **Remediation**: Specific actions to resolve and prevent recurrence
   - **Urgency**: Is this ongoing or already resolved?

Prioritize anomalies by dollar impact, with ongoing anomalies flagged as urgent.`,
                    },
                }],
        };
    });
}
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