clean mcp schema for gemini models, update http mcp example to use gemini

examples/foundational/39c-mcp-run-http.py

@@ -16,6 +16,7 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.google.llm import GoogleLLMService
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.mcp_service import MCPClient
@@ -58,7 +59,9 @@ async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_si
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini")
+    llm = GoogleLLMService(
+        api_key=os.getenv("GOOGLE_API_KEY"), model="gemini-2.0-flash"
+    )
 
     try:
         # Github MCP docs: https://github.com/github/github-mcp-server

src/pipecat/services/mcp_service.py

@@ -82,13 +82,14 @@ class MCPClient(BaseObject):
         return tools_schema
 
     def _convert_mcp_schema_to_pipecat(
-        self, tool_name: str, tool_schema: Dict[str, Any]
+        self, tool_name: str, tool_schema: Dict[str, Any], llm=None
     ) -> FunctionSchema:
         """Convert an mcp tool schema to Pipecat's FunctionSchema format.
 
         Args:
             tool_name: The name of the tool
             tool_schema: The mcp tool schema
+            llm: The LLM service instance (used to determine if we need Gemini compatibility)
         Returns:
             A FunctionSchema instance
         """
@@ -97,6 +98,11 @@ class MCPClient(BaseObject):
 
         properties = tool_schema["input_schema"].get("properties", {})
         required = tool_schema["input_schema"].get("required", [])
+        
+        # Only clean properties for Google/Gemini LLM services
+        if llm and self._is_google_llm(llm):
+            logger.debug(f"Detected Google LLM service, cleaning schema for Gemini compatibility")
+            properties = self._clean_schema_for_gemini(properties)
 
         schema = FunctionSchema(
             name=tool_name,