fix(agent_tool): wrap input_schema JSON in ReAct prompt; propagate tool_choice to LiteLLM

When AgentTool uses input_schema, the inner agent receives a raw JSON blob
that causes Claude models to skip the tool-calling loop (ReAct). Fix by
wrapping the payload in a natural-language instruction.

Also propagate tool_config.function_calling_config.mode to LiteLLM's
tool_choice parameter so callers can enforce tool_choice='required'.
Addresses #773.

Fixes: #5926

Author: ecanlar

src/google/adk/models/lite_llm.py

@@ -1883,6 +1883,7 @@ async def _get_completion_inputs(
     Optional[List[Dict]],
     Optional[Dict[str, Any]],
     Optional[Dict],
+    Optional[str],
 ]:
   """Converts an LlmRequest to litellm inputs and extracts generation params.
 
@@ -1891,8 +1892,8 @@ async def _get_completion_inputs(
     model: The model string to use for determining provider-specific behavior.
 
   Returns:
-    The litellm inputs (message list, tool dictionary, response format and
-    generation params).
+    The litellm inputs (message list, tool dictionary, response format,
+    generation params, and tool_choice).
   """
   _ensure_litellm_imported()
 
@@ -1967,7 +1968,21 @@ async def _get_completion_inputs(
     if not generation_params:
       generation_params = None
 
-  return messages, tools, response_format, generation_params
+  # 5. Extract tool_choice from tool_config
+  tool_choice: Optional[str] = None
+  if (
+      llm_request.config
+      and llm_request.config.tool_config
+      and llm_request.config.tool_config.function_calling_config
+  ):
+    mode = llm_request.config.tool_config.function_calling_config.mode
+    if mode == types.FunctionCallingConfigMode.ANY:
+      tool_choice = "required"
+    elif mode == types.FunctionCallingConfigMode.NONE:
+      tool_choice = "none"
+    # AUTO → None (provider default)
+
+  return messages, tools, response_format, generation_params, tool_choice
 
 
 def _build_function_declaration_log(
@@ -2228,7 +2243,7 @@ async def generate_content_async(
       logger.debug(_build_request_log(llm_request))
 
     effective_model = llm_request.model or self.model
-    messages, tools, response_format, generation_params = (
+    messages, tools, response_format, generation_params, tool_choice = (
         await _get_completion_inputs(llm_request, effective_model)
     )
     normalized_messages = _normalize_ollama_chat_messages(
@@ -2260,6 +2275,9 @@ async def generate_content_async(
     if generation_params:
       completion_args.update(generation_params)
 
+    if tool_choice is not None:
+      completion_args["tool_choice"] = tool_choice
+
     if stream:
       text = ""
       reasoning_parts: List[types.Part] = []

src/google/adk/tools/agent_tool.py

@@ -217,11 +217,17 @@ async def run_async(
     input_schema = _get_input_schema(self.agent)
     if input_schema:
       input_value = input_schema.model_validate(args)
+      json_payload = input_value.model_dump_json(exclude_none=True)
       content = types.Content(
           role='user',
           parts=[
               types.Part.from_text(
-                  text=input_value.model_dump_json(exclude_none=True)
+                  text=(
+                      'Process the following structured request. Use your'
+                      ' available tools as needed to gather information or'
+                      ' perform actions before producing the final'
+                      ' response.\n\nRequest:\n' + json_payload
+                  )
               )
           ],
       )