diff --git a/llama_stack/providers/inline/agents/meta_reference/responses/streaming.py b/llama_stack/providers/inline/agents/meta_reference/responses/streaming.py
index 026ce6395..daac995ca 100644
--- a/llama_stack/providers/inline/agents/meta_reference/responses/streaming.py
+++ b/llama_stack/providers/inline/agents/meta_reference/responses/streaming.py
@@ -47,6 +47,7 @@ from llama_stack.apis.inference import (
     OpenAIMessageParam,
 )
 from llama_stack.log import get_logger
+from llama_stack.providers.utils.telemetry import tracing
 
 from .types import ChatCompletionContext, ChatCompletionResult
 from .utils import convert_chat_choice_to_response_message, is_function_tool_call
@@ -597,14 +598,22 @@ class StreamingResponseOrchestrator:
                     never_allowed = mcp_tool.allowed_tools.never
 
             # Call list_mcp_tools
-            tool_defs = await list_mcp_tools(
-                endpoint=mcp_tool.server_url,
-                headers=mcp_tool.headers or {},
-            )
+            tool_defs = None
+            list_id = f"mcp_list_{uuid.uuid4()}"
+            attributes = {
+                "server_label": mcp_tool.server_label,
+                "server_url": mcp_tool.server_url,
+                "mcp_list_tools_id": list_id,
+            }
+            async with tracing.span("list_mcp_tools", attributes):
+                tool_defs = await list_mcp_tools(
+                    endpoint=mcp_tool.server_url,
+                    headers=mcp_tool.headers or {},
+                )
 
             # Create the MCP list tools message
             mcp_list_message = OpenAIResponseOutputMessageMCPListTools(
-                id=f"mcp_list_{uuid.uuid4()}",
+                id=list_id,
                 server_label=mcp_tool.server_label,
                 tools=[],
             )
diff --git a/llama_stack/providers/inline/agents/meta_reference/responses/tool_executor.py b/llama_stack/providers/inline/agents/meta_reference/responses/tool_executor.py
index b33b47454..0a9967fe5 100644
--- a/llama_stack/providers/inline/agents/meta_reference/responses/tool_executor.py
+++ b/llama_stack/providers/inline/agents/meta_reference/responses/tool_executor.py
@@ -35,6 +35,7 @@ from llama_stack.apis.inference import (
 from llama_stack.apis.tools import ToolGroups, ToolInvocationResult, ToolRuntime
 from llama_stack.apis.vector_io import VectorIO
 from llama_stack.log import get_logger
+from llama_stack.providers.utils.telemetry import tracing
 
 from .types import ChatCompletionContext, ToolExecutionResult
 
@@ -251,12 +252,18 @@ class ToolExecutor:
                 from llama_stack.providers.utils.tools.mcp import invoke_mcp_tool
 
                 mcp_tool = mcp_tool_to_server[function_name]
-                result = await invoke_mcp_tool(
-                    endpoint=mcp_tool.server_url,
-                    headers=mcp_tool.headers or {},
-                    tool_name=function_name,
-                    kwargs=tool_kwargs,
-                )
+                attributes = {
+                    "server_label": mcp_tool.server_label,
+                    "server_url": mcp_tool.server_url,
+                    "tool_name": function_name,
+                }
+                async with tracing.span("invoke_mcp_tool", attributes):
+                    result = await invoke_mcp_tool(
+                        endpoint=mcp_tool.server_url,
+                        headers=mcp_tool.headers or {},
+                        tool_name=function_name,
+                        kwargs=tool_kwargs,
+                    )
             elif function_name == "knowledge_search":
                 response_file_search_tool = next(
                     (t for t in ctx.response_tools if isinstance(t, OpenAIResponseInputToolFileSearch)),
@@ -266,15 +273,20 @@ class ToolExecutor:
                     # Use vector_stores.search API instead of knowledge_search tool
                     # to support filters and ranking_options
                     query = tool_kwargs.get("query", "")
-                    result = await self._execute_knowledge_search_via_vector_store(
-                        query=query,
-                        response_file_search_tool=response_file_search_tool,
-                    )
+                    async with tracing.span("knowledge_search", {}):
+                        result = await self._execute_knowledge_search_via_vector_store(
+                            query=query,
+                            response_file_search_tool=response_file_search_tool,
+                        )
             else:
-                result = await self.tool_runtime_api.invoke_tool(
-                    tool_name=function_name,
-                    kwargs=tool_kwargs,
-                )
+                attributes = {
+                    "tool_name": function_name,
+                }
+                async with tracing.span("invoke_tool", attributes):
+                    result = await self.tool_runtime_api.invoke_tool(
+                        tool_name=function_name,
+                        kwargs=tool_kwargs,
+                    )
         except Exception as e:
             error_exc = e