feat(local-llm): B1b-Plumbing — tools/tool_calls durch Adapter/Bridge/Brain-Client

Traegt OpenAI-Tool-Definitionen (tools) durch den ganzen lokalen Pfad und gibt
tool_calls zurueck:
- adapter.py: tools -> llama.cpp /v1/chat/completions (tool_choice=auto),
  message.tool_calls zurueck in llm_response.
- aria_bridge.py: _local_llm + /internal/local-llm reichen tools durch, geben
  tool_calls zurueck.
- local_llm.py: local_llm_chat akzeptiert tools, result enthaelt tool_calls.

Inert bis der Brain-Tool-Loop (naechster Schritt) tools uebergibt — Verhalten
unveraendert. Tool-Set + lokale Tool-Loop + Router-Anpassung folgen.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

xtts/llm-adapter/adapter.py

@@ -69,9 +69,12 @@ async def _send(ws, mtype: str, payload: dict) -> None:
 
 
 async def _call_llama(messages: list, *, max_tokens: int, temperature: float,
-                      stop) -> dict:
+                      stop, tools=None) -> dict:
     """Ruft llama.cpp /v1/chat/completions (OpenAI-Format). Gibt
-    {ok, content, error} zurueck — wirft nie."""
+    {ok, content, tool_calls, error} zurueck — wirft nie.
+
+    tools: optionale OpenAI-Tool-Definitionen (B1b). llama.cpp (--jinja) mit
+    Qwen3 kann natives Tool-Calling und liefert dann message.tool_calls."""
     body = {
         "model": LLM_MODEL,
         "messages": messages,
@@ -81,6 +84,9 @@ async def _call_llama(messages: list, *, max_tokens: int, temperature: float,
     }
     if stop:
         body["stop"] = stop
+    if tools:
+        body["tools"] = tools
+        body["tool_choice"] = "auto"
     if LLM_DISABLE_THINKING:
         # llama.cpp (--jinja) reicht chat_template_kwargs an die Chat-Vorlage
         # weiter. Qwen3 unterdrueckt damit den <think>-Block.
@@ -90,8 +96,13 @@ async def _call_llama(messages: list, *, max_tokens: int, temperature: float,
             r = await client.post(f"{LLAMA_URL}/v1/chat/completions", json=body)
             r.raise_for_status()
             data = r.json()
-        content = (data.get("choices") or [{}])[0].get("message", {}).get("content", "")
-        return {"ok": True, "content": content or "", "usage": data.get("usage")}
+        msg = (data.get("choices") or [{}])[0].get("message", {}) or {}
+        return {
+            "ok": True,
+            "content": msg.get("content") or "",
+            "tool_calls": msg.get("tool_calls") or None,
+            "usage": data.get("usage"),
+        }
     except Exception as e:
         logger.warning("llama.cpp-Call fehlgeschlagen: %s", e)
         return {"ok": False, "content": "", "error": str(e)[:300]}
@@ -108,17 +119,20 @@ async def _handle_llm_request(ws, payload: dict) -> None:
     max_tokens = int(payload.get("max_tokens", 512) or 512)
     temperature = float(payload.get("temperature", 0.7) or 0.7)
     stop = payload.get("stop")
+    tools = payload.get("tools") or None
     t0 = time.time()
     res = await _call_llama(messages, max_tokens=max_tokens,
-                            temperature=temperature, stop=stop)
+                            temperature=temperature, stop=stop, tools=tools)
     dt = time.time() - t0
-    logger.info("llm_request id=%s -> ok=%s %.2fs content_len=%d",
+    tc = res.get("tool_calls")
+    logger.info("llm_request id=%s -> ok=%s %.2fs content_len=%d tool_calls=%d",
                 (req_id[:8] if req_id else "?"), res.get("ok"), dt,
-                len(res.get("content") or ""))
+                len(res.get("content") or ""), len(tc) if tc else 0)
     await _send(ws, "llm_response", {
         "requestId": req_id,
         "ok": res.get("ok", False),
         "content": res.get("content", ""),
+        "tool_calls": tc,
         "error": res.get("error"),
         "model": LLM_MODEL,
         "elapsedMs": int(dt * 1000),