refactor(ai_client): rename send_result to send in 5 src/ call sites

Renames 10 references across app_controller, conductor_tech_lead,
mcp_client (docstring example), multi_agent_conductor, orchestrator_pm.

5 call sites in ai_client.send_result(...) -> ai_client.send(...)
3 print strings mentioning send_result
1 docstring comment (conductor_tech_lead)
1 docstring example (mcp_client) 'src.ai_client.send_result' -> 'src.ai_client.send'

Test suite state: still red, but all src/-level call sites are now
renamed. Remaining failures are in test files (mocks and patches
that still reference send_result).

Refs: conductor/tracks/send_result_to_send_20260616/

scripts/tier2/apply_t2_1_edits.py

@@ -0,0 +1,69 @@
+"""Apply the 10 send_result -> send edits in the 5 other src/ files (Phase 2)."""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+FILES = [
+ "src/app_controller.py",
+ "src/conductor_tech_lead.py",
+ "src/mcp_client.py",
+ "src/multi_agent_conductor.py",
+ "src/orchestrator_pm.py",
+]
+
+EDITS: dict[str, list[tuple[str, str]]] = {
+ "src/app_controller.py": [
+  ("result = ai_client.send_result(context_to_send,", "result = ai_client.send(context_to_send,"),
+  ("result = ai_client.send_result(\n", "result = ai_client.send(\n"),
+ ],
+ "src/conductor_tech_lead.py": [
+  (" - Uses ai_client.send_result() for LLM communication", " - Uses ai_client.send() for LLM communication"),
+  ("result = ai_client.send_result(\n", "result = ai_client.send(\n"),
+  ("print(f\"[conductor_tech_lead] send_result failed: {_msg}\")", "print(f\"[conductor_tech_lead] send failed: {_msg}\")"),
+ ],
+ "src/mcp_client.py": [
+  ("'src.ai_client.send_result'", "'src.ai_client.send'"),
+ ],
+ "src/multi_agent_conductor.py": [
+  ("result = ai_client.send_result(\n", "result = ai_client.send(\n"),
+  ("print(f\"[MMA] Worker send_result failed for {ticket.id}: {err_msg}\")", "print(f\"[MMA] Worker send failed for {ticket.id}: {err_msg}\")"),
+ ],
+ "src/orchestrator_pm.py": [
+  ("result = ai_client.send_result(\n", "result = ai_client.send(\n"),
+  ("print(f\"[orchestrator_pm] send_result failed: {_msg}\")", "print(f\"[orchestrator_pm] send failed: {_msg}\")"),
+ ],
+}
+
+
+def main() -> int:
+ total = 0
+ for rel in FILES:
+  p = Path(rel)
+  with p.open("r", encoding="utf-8", newline="") as f:
+   content = f.read()
+  has_crlf = "\r\n" in content
+  nl = "\r\n" if has_crlf else "\n"
+  edits = [(o.replace("\n", nl), n.replace("\n", nl)) for o, n in EDITS[rel]]
+  new_content = content
+  applied = 0
+  for old, new in edits:
+   if old in new_content:
+    new_content = new_content.replace(old, new, 1)
+    applied += 1
+   else:
+    print(f"NOT FOUND in {rel}: {old[:80]!r}", file=sys.stderr)
+  if applied != len(edits):
+   print(f"Only applied {applied}/{len(edits)} edits in {rel}. ABORTING.", file=sys.stderr)
+   return 1
+  with p.open("w", encoding="utf-8", newline="") as f:
+   f.write(new_content)
+  remaining = new_content.count("send_result")
+  print(f"{rel}: applied {applied}/{len(edits)}, remaining={remaining}")
+  total += applied
+ print(f"Total: {total} edits applied")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())

src/app_controller.py

@@ -279,7 +279,7 @@ def _api_generate(controller: 'AppController', req: GenerateRequest) -> dict[str
     has_ai_response = any(e.get("role") == "AI" for e in controller.disc_entries)
    context_to_send = stable_md if not has_ai_response else ""
 
-   result = ai_client.send_result(context_to_send, user_msg, base_dir, controller.last_file_items, disc_text, rag_engine=None)
+   result = ai_client.send(context_to_send, user_msg, base_dir, controller.last_file_items, disc_text, rag_engine=None)
    if not result.ok:
     err = result.errors[0]
     raise HTTPException(status_code=502, detail=err.ui_message())
@@ -3671,7 +3671,7 @@ class AppController:
   self._update_gcli_adapter(self.ui_gemini_cli_path)
   # FR2 / Bug #1: per conductor/code_styleguides/error_handling.md section 3.1 (AND over OR),
   # we check result.ok instead of catching a ProviderError exception.
-  result = ai_client.send_result(
+  result = ai_client.send(
    event.stable_md,
    user_msg,
    event.base_dir,

src/conductor_tech_lead.py

@@ -5,7 +5,7 @@ This module implements the Tier 2 (Tech Lead) function for generating implementa
  It uses the LLM to analyze the track requirements and produce structured ticket definitions.
 
 Architecture:
- - Uses ai_client.send_result() for LLM communication
+ - Uses ai_client.send() for LLM communication
  - Uses mma_prompts.PROMPTS["tier2_sprint_planning"] for system prompt
  - Returns JSON array of ticket definitions
 
@@ -65,14 +65,14 @@ def generate_tickets(track_brief: str, module_skeletons: str) -> list[dict[str,
   for _ in range(3):
    try:
    # 3. Call Tier 2 Model
-    result = ai_client.send_result(
+    result = ai_client.send(
      md_content   = "",
      user_message = user_message
     )
     if not result.ok:
      _err = result.errors[0] if result.errors else None
      _msg = _err.ui_message() if _err else "unknown error"
-     print(f"[conductor_tech_lead] send_result failed: {_msg}")
+     print(f"[conductor_tech_lead] send failed: {_msg}")
      return None
     response = result.data
     # 4. Parse JSON Output

src/mcp_client.py

@@ -2370,7 +2370,7 @@ MCP_TOOL_SPECS: list[dict[str, Any]] = [
    "properties": {
     "target": {
      "type": "string",
-     "description": "Fully qualified name of the target (e.g., 'src.ai_client.send_result') or class.method.",
+     "description": "Fully qualified name of the target (e.g., 'src.ai_client.send') or class.method.",
     },
     "max_depth": {
      "type": "integer",

src/multi_agent_conductor.py

@@ -588,7 +588,7 @@ def run_worker_lifecycle(ticket: Ticket, context: WorkerContext, context_files:
  ai_client.set_current_tier(f"Tier 3 (Worker): {ticket.id}")
  try:
   comms_baseline = len(ai_client.get_comms_log())
-  result = ai_client.send_result(
+  result = ai_client.send(
    md_content=md_content,
    user_message=user_message,
    base_dir=".",
@@ -600,7 +600,7 @@ def run_worker_lifecycle(ticket: Ticket, context: WorkerContext, context_files:
   if not result.ok:
    err = result.errors[0] if result.errors else None
    err_msg = err.ui_message() if err else "unknown error"
-   print(f"[MMA] Worker send_result failed for {ticket.id}: {err_msg}")
+   print(f"[MMA] Worker send failed for {ticket.id}: {err_msg}")
    if event_queue:
     _queue_put(event_queue, "response", {"text": f"\n\n[ERROR] {err_msg}", "stream_id": f"Tier 3 (Worker): {ticket.id}", "status": "error", "role": "Vendor API"})
     _queue_put(event_queue, "ticket_completed", {"ticket_id": ticket.id, "timestamp": time.time()})

src/orchestrator_pm.py

@@ -83,7 +83,7 @@ def generate_tracks(user_request: str, project_config: dict[str, Any], file_item
  try:
  # 3. Call Tier 1 Model (Strategic - Pro)
  # Note: We use gemini-1.5-pro or similar high-reasoning model for Tier 1
-  result = ai_client.send_result(
+  result = ai_client.send(
    md_content="", # We pass everything in user_message for clarity
    user_message=user_message,
    enable_tools=False,
@@ -91,7 +91,7 @@ def generate_tracks(user_request: str, project_config: dict[str, Any], file_item
   if not result.ok:
    _err = result.errors[0] if result.errors else None
    _msg = _err.ui_message() if _err else "unknown error"
-   print(f"[orchestrator_pm] send_result failed: {_msg}")
+   print(f"[orchestrator_pm] send failed: {_msg}")
    return []
   response = result.data
   # 4. Parse JSON Output