feat(ai): scope persistence, parallel tools, Prometheus token/cache counters

C2 full scope persistence:
- getRecentConversationContext now returns per-turn guildId/channelId
- processMessage merges historical scope when current request is unscoped
- chatbot remembers server context across all 8 history exchanges (not just 3)

A4+Metrics:
- Add incrementCounterBy(name, delta, labels) to gateway-metrics
- Token-usage counters: llm_tokens_total{model, type, label} per batch
- Cache hit counters: moderation_cache_hits{type} exact/semantic-qdrant/semantic-pg
- Cache miss counters: moderation_cache_misses per batch
- Prometheus /metrics now exposes cost + cache hit-rate for dashboards

Performance:
- Parallel tool execution within each chatbot round (Promise.all)
- All tool results collected before sending to model (ordering preserved)
- Tool failure now logged with structured warning (chatbot.tools.ts)

Verified: backend tsc+biome 37/37, discord-gateway tsc+biome 210/210

services/discord-gateway/src/modules/ai-moderation/llmCaller.ts

@@ -14,6 +14,7 @@ import type { ChatCompletion } from "openai/resources/chat/completions";
 import { createChildLogger } from "@/shared/logger/index";
 import { delay, retryWithBackoff } from "@/shared/utils/index";
 import { config } from "../../shared/config/config.js";
+import { incrementCounterBy } from "../gateway-metrics/index.js";
 import type { AnalysisResult } from "../message-capture/types.js";
 import { llmChat } from "./llmClient.js";
 import { parseModerationResponse } from "./moderationResponseParser.js";
@@ -170,6 +171,23 @@ export async function callModerationLLM(
     // so cost per channel/guild can be tracked (routers bill per token).
     const usage = result?.usage;
     if (usage && (usage.prompt_tokens || usage.completion_tokens)) {
+      // Prometheus token-usage counters for cost tracking per model + phase.
+      // Counters accumulate across scrapes so total spend per model/label is
+      // queryable (rate(...) dashboards).
+      if (usage.prompt_tokens) {
+        incrementCounterBy("llm_tokens_total", usage.prompt_tokens, {
+          model: config.AI_LLM_MODEL,
+          type: "prompt",
+          label,
+        });
+      }
+      if (usage.completion_tokens) {
+        incrementCounterBy("llm_tokens_total", usage.completion_tokens, {
+          model: config.AI_LLM_MODEL,
+          type: "completion",
+          label,
+        });
+      }
       log.info(
         {
           label,