refactor(minimax): restore tool-call loop in _send_minimax

The previous refactor (commit 344a66fc) dropped the tool-call loop
in _send_minimax. The original function executed tool calls when the
response had tool_calls; the refactor was single-shot. This is a real
behavior regression (tools stop working) even though the existing
tests don't catch it.

Restore the tool loop:
- For each round (up to MAX_TOOL_ROUNDS + 2), call send_openai_compatible
  with tools=_get_deepseek_tools() and tool_choice='auto'
- If response has tool_calls: dispatch each via
  _execute_tool_calls_concurrently (handles both async context and
  sync via run_coroutine_threadsafe / asyncio.run), append each
  result to _minimax_history with role='tool' and tool_call_id
- If no tool_calls: return the response text (with thinking tags for
  reasoning models)
- The lock is acquired/released per iteration to avoid holding it
  during the API call (which can take seconds)

Preserved:
- 10-arg signature
- _minimax_history_lock (now acquired per iteration)
- _repair_minimax_history
- discussion_history handling
- System + context message wrapping
- Reasoning content extraction (response.raw_response.choices[0].message
  .reasoning_details[0].get('text', ''))
- <thinking> tags wrap on the final response

Dropped (still):
- extra_body={reasoning_split: True} (not supported by send_openai_compatible;
  would be a Phase 5 adapter addition if minimax-reasoner models need it)

New line count: 75 lines (vs 41 single-shot, vs 231 pre-refactor).
Net effect: 231 -> 75 = 68% reduction; tool loop preserved.

Verification: 38/38 tests pass (no regressions).

src/ai_client.py

@@ -2233,40 +2233,63 @@ def _send_minimax(md_content: str, user_message: str, base_dir: str,
  _ensure_minimax_client()
  from src.openai_compatible import OpenAICompatibleRequest, send_openai_compatible
  from src.vendor_capabilities import get_capabilities
+ tools: list[dict[str, Any]] | None = _get_deepseek_tools() or None
  with _minimax_history_lock:
   _repair_minimax_history(_minimax_history)
   if discussion_history and not _minimax_history:
    _minimax_history.append({"role": "user", "content": f"[DISCUSSION HISTORY]\n\n{discussion_history}\n\n---\n\n{user_message}"})
   else:
    _minimax_history.append({"role": "user", "content": user_message})
-  messages = [{"role": "system", "content": f"{_get_combined_system_prompt()}\n\n<context>\n{md_content}\n</context>"}]
-  messages.extend(_minimax_history)
- request = OpenAICompatibleRequest(
-  messages=messages,
-  model=_model,
-  temperature=_temperature,
-  top_p=_top_p,
-  max_tokens=min(_max_tokens, 8192),
-  stream=stream,
-  stream_callback=stream_callback,
- )
- caps = get_capabilities("minimax", _model)
- response = send_openai_compatible(_minimax_client, request, capabilities=caps)
- reasoning_content = ""
- if response.raw_response and hasattr(response.raw_response, "choices"):
-  choice = response.raw_response.choices[0]
-  if hasattr(choice.message, "reasoning_details") and choice.message.reasoning_details:
-   reasoning_content = choice.message.reasoning_details[0].get("text", "") if choice.message.reasoning_details else ""
- thinking_tags = ""
- if reasoning_content:
-  thinking_tags = f"<thinking>\n{reasoning_content}\n</thinking>\n"
- full_text = thinking_tags + response.text
- with _minimax_history_lock:
-  msg_to_store: dict[str, Any] = {"role": "assistant", "content": response.text or None}
-  if reasoning_content:
-   msg_to_store["reasoning_content"] = reasoning_content
-  _minimax_history.append(msg_to_store)
- return full_text
+ response_text: str = ""
+ reasoning_content: str = ""
+ for round_idx in range(MAX_TOOL_ROUNDS + 2):
+  with _minimax_history_lock:
+   messages = [{"role": "system", "content": f"{_get_combined_system_prompt()}\n\n<context>\n{md_content}\n</context>"}]
+   messages.extend(_minimax_history)
+  request = OpenAICompatibleRequest(
+   messages=messages,
+   model=_model,
+   temperature=_temperature,
+   top_p=_top_p,
+   max_tokens=min(_max_tokens, 8192),
+   stream=stream,
+   stream_callback=stream_callback,
+   tools=tools,
+   tool_choice="auto" if tools else "auto",
+  )
+  caps = get_capabilities("minimax", _model)
+  response = send_openai_compatible(_minimax_client, request, capabilities=caps)
+  reasoning_content = ""
+  if response.raw_response and hasattr(response.raw_response, "choices"):
+   choice = response.raw_response.choices[0]
+   if hasattr(choice.message, "reasoning_details") and choice.message.reasoning_details:
+    reasoning_content = choice.message.reasoning_details[0].get("text", "") if choice.message.reasoning_details else ""
+  with _minimax_history_lock:
+   msg_to_store: dict[str, Any] = {"role": "assistant", "content": response.text or None}
+   if reasoning_content:
+    msg_to_store["reasoning_content"] = reasoning_content
+   if response.tool_calls:
+    msg_to_store["tool_calls"] = response.tool_calls
+   _minimax_history.append(msg_to_store)
+  if not response.tool_calls:
+   response_text = (f"<thinking>\n{reasoning_content}\n</thinking>\n" if reasoning_content else "") + response.text
+   break
+  try:
+   loop = asyncio.get_running_loop()
+   results = asyncio.run_coroutine_threadsafe(
+    _execute_tool_calls_concurrently(response.tool_calls, base_dir, pre_tool_callback, qa_callback, round_idx, "minimax", patch_callback),
+    loop,
+   ).result()
+  except RuntimeError:
+   results = asyncio.run(_execute_tool_calls_concurrently(response.tool_calls, base_dir, pre_tool_callback, qa_callback, round_idx, "minimax", patch_callback))
+  with _minimax_history_lock:
+   for _i, (name, call_id, out, _) in enumerate(results):
+    _minimax_history.append({
+     "role": "tool",
+     "tool_call_id": call_id,
+     "content": str(out) if out else "",
+    })
+ return response_text
 
 #endregion: MiniMax Provider