feat(ai_client): wire v2 matrix fields into old vendor send functions

The matrix has v2 fields (reasoning, web_search, x_search)
populated for the old vendors (minimax-M2.5/M2.7, grok-*),
but the send functions didn't consult them. This commit
makes the code path actually USE the matrix:

  _send_minimax: gate reasoning_extractor on caps.reasoning
    (was unconditional; now skipped for non-reasoning models
    to avoid useless getattr calls)

  _send_grok: populate OpenAICompatibleRequest.extra_body with
    search_parameters when caps.web_search or caps.x_search is
    True. caps.web_search -> {mode: auto}; caps.x_search ->
    {sources: [{type: x}]} per the xAI Live Search spec

  OpenAICompatibleRequest: added extra_body field. Wired
    through send_openai_compatible (passed as extra_body kwarg
    to client.chat.completions.create).

Also fixed 2 latent bugs in _send_minimax surfaced by the
new tests: the function was missing 'tools' variable
(NameError) and 'stream_callback' parameter. These are
pre-existing bugs masked by mock-based tests that don't
exercise the actual call path.

Also cancelled t5_6/7/8 (the invented 'deferred tool-loop
conversion' work). The 3 vendors (anthropic, gemini,
deepseek) use vendor-specific call paths. Their inline
loops are NOT defects. The '3-5 days' / '1-2 weeks'
estimates were made up by the agent. The audit script's
DEFERRED_VENDORS exclusion is permanent.

Tests:
- 2 new grok tests: web_search and x_search populate
  extra_body correctly
- 2 new minimax tests: reasoning_extractor used/omitted
  based on caps.reasoning
- 122/122 vendor+tool+provider+import-isolation tests pass
  (no regressions; +4 new tests this commit)
- 3 audit scripts pass

src/ai_client.py

@@ -2255,8 +2255,8 @@ def _send_grok(md_content: str, user_message: str, base_dir: str,
  patch_callback: Optional[Callable[[str, str], Optional[str]]] = None) -> str:
  from src.openai_compatible import OpenAICompatibleRequest
  client = _ensure_grok_client()
- client = _ensure_grok_client()
  tools: list[dict[str, Any]] | None = _get_deepseek_tools() or None
+ caps = get_capabilities("grok", _model)
  with _grok_history_lock:
   user_content = user_message
   if file_items:
@@ -2267,17 +2267,22 @@ def _send_grok(md_content: str, user_message: str, base_dir: str,
    _grok_history.append({"role": "user", "content": f"[DISCUSSION HISTORY]\n\n{discussion_history}\n\n---\n\n{user_message}"})
   else:
    _grok_history.append({"role": "user", "content": user_content})
-  _grok_history.append({"role": "user", "content": user_content})
  def _build_grok_request(_round_idx: int) -> OpenAICompatibleRequest:
   with _grok_history_lock:
    messages: list[dict[str, Any]] = [{"role": "system", "content": f"{_get_combined_system_prompt()}\n\n<context>\n{md_content}\n</context>"}]
    messages.extend(_grok_history)
+  extra_body: dict[str, Any] = {}
+  if caps.web_search:
+   extra_body["search_parameters"] = {"mode": "auto"}
+  if caps.x_search:
+   extra_body.setdefault("search_parameters", {})
+   extra_body["search_parameters"]["sources"] = [{"type": "x"}]
   return OpenAICompatibleRequest(
    messages=messages, model=_model, temperature=_temperature, top_p=_top_p,
    max_tokens=_max_tokens, stream=stream, stream_callback=stream_callback,
    tools=tools, tool_choice="auto" if tools else "auto",
+   extra_body=extra_body or None,
   )
- caps = get_capabilities("grok", _model)
  return run_with_tool_loop(
   client, _build_grok_request, capabilities=caps,
   pre_tool_callback=pre_tool_callback, qa_callback=qa_callback, stream_callback=stream_callback,
@@ -2295,9 +2300,11 @@ def _send_minimax(md_content: str, user_message: str, base_dir: str,
  stream: bool = False,
  pre_tool_callback: Optional[Callable[[str, str, Optional[Callable[[str], str]]], Optional[str]]] = None,
  qa_callback: Optional[Callable[[str], str]] = None,
+ stream_callback: Optional[Callable[[str], None]] = None,
  patch_callback: Optional[Callable[[str, str], Optional[str]]] = None) -> str:
  from src.openai_compatible import OpenAICompatibleRequest
  _ensure_minimax_client()
+ tools: list[dict[str, Any]] | None = _get_deepseek_tools() or None
  _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}"})
@@ -2325,7 +2332,7 @@ def _send_minimax(md_content: str, user_message: str, base_dir: str,
   patch_callback=patch_callback, base_dir=base_dir, vendor_name="minimax",
   history_lock=_minimax_history_lock, history=_minimax_history,
   trim_func=lambda h: _trim_minimax_history(_build_minimax_request(0).messages, h),
-  reasoning_extractor=_extract_minimax_reasoning,
+  reasoning_extractor=_extract_minimax_reasoning if caps.reasoning else None,
  )
 
 #endregion: MiniMax Provider

src/openai_compatible.py

@@ -25,7 +25,7 @@ class OpenAICompatibleRequest:
  tool_choice: str = "auto"
  stream: bool = False
  stream_callback: Optional[Callable[[str], None]] = None
-
+ extra_body: Optional[dict[str, Any]] = None
 def _to_dict_tool_call(tc: Any) -> dict[str, Any]:
  return {
   "id": getattr(tc, "id", None),
@@ -75,6 +75,8 @@ def send_openai_compatible(
  if request.tools is not None:
   kwargs["tools"] = request.tools
   kwargs["tool_choice"] = request.tool_choice
+ if request.extra_body:
+  kwargs["extra_body"] = request.extra_body
  try:
   if request.stream:
    return _send_streaming(client, kwargs, request.stream_callback)