Merge branch 'main' into security/ssrf-embedded-ipv4-block

Author: rohityan

contributing/samples/adk_team/adk_documentation/adk_release_analyzer/agent.py

@@ -559,6 +559,7 @@ def summary_instruction(readonly_context: ReadonlyContext) -> str:
     name="summary_agent",
     description="Compiles recommendations and creates the GitHub issue.",
     instruction=summary_instruction,
+    include_contents="none",
     tools=[
         get_all_recommendations,
         create_issue,

contributing/samples/managed_agent/basic/README.md

@@ -1,5 +1,8 @@
 # Managed Agent
 
+> For setup, authentication, backends, and background on `ManagedAgent`, see the
+> [ManagedAgent guide](../../../../docs/guides/agents/managed_agent/index.md).
+
 ## Overview
 
 This sample runs a `ManagedAgent` configured with the built-in `google_search`

contributing/samples/managed_agent/code_execution/README.md

@@ -1,5 +1,8 @@
 # Managed Agent - Code Execution
 
+> For setup, authentication, backends, and background on `ManagedAgent`, see the
+> [ManagedAgent guide](../../../../docs/guides/agents/managed_agent/index.md).
+
 ## Overview
 
 This sample runs a `ManagedAgent` configured with the built-in **code execution**

docs/guides/README.md

@@ -13,6 +13,9 @@ This directory contains specific developer guides for the ADK Python implementat
 * [Event and NodeInfo](events/event/index.md) - Understanding Event and NodeInfo in workflows.
 * [RequestInput](events/request_input/index.md) - How to use RequestInput for human-in-the-loop interactions.
 
+### Tools
+* [to_mcp_server](tools/mcp_tool/agent_to_mcp/index.md) - Expose an ADK agent as an MCP server so any MCP host can drive it as a single tool (the MCP counterpart of to_a2a).
+
 ### Workflows
 * [Workflow](workflow/workflow/index.md) - Graph-based orchestration of complex, multi-step agent interactions.
 * [Workflow Graphs](workflow/graph/index.md) - Understanding nodes, edges, and graph structures in workflows.

docs/guides/tools/mcp_tool/agent_to_mcp/index.md

@@ -0,0 +1,139 @@
+# to_mcp_server
+
+Exposes an ADK agent as an MCP server so any MCP host (Claude Code, OpenAI
+Codex, an IDE, or any MCP client) can drive it as a single tool. It is the MCP
+counterpart of `to_a2a`.
+
+## Introduction
+
+`to_mcp_server` turns a whole ADK agent into a standard
+[Model Context Protocol](https://modelcontextprotocol.io/) server. The agent —
+its model loop and all of its tools — is registered as a *single* MCP tool named
+after the agent. A host that speaks MCP sends a request string and receives the
+agent's final response; it never imports ADK and does not see the agent's
+individual tools.
+
+This solves the problem of making an ADK agent consumable by harnesses that are
+not ADK. Where `to_a2a` publishes an agent over A2A, `to_mcp_server` publishes it
+over MCP, so coding agents and IDEs that already speak MCP can delegate a task to
+an ADK agent as if it were any other tool. It builds on `Runner` to execute the
+agent and returns a `FastMCP` server, leaving the choice of transport (stdio for
+local hosts, streamable-http for networked ones) to the caller.
+
+## Get started
+
+Define an agent and expose it. Running the file starts the MCP server on stdio;
+an MCP host can also launch it as a subprocess.
+
+```python
+import random
+
+from google.adk.agents import LlmAgent
+from google.adk.tools.mcp_tool import to_mcp_server
+
+
+def roll_die(sides: int) -> int:
+  """Roll a die with the given number of sides and return the result."""
+  return random.randint(1, sides)
+
+
+dice_agent = LlmAgent(
+    name="dice_agent",
+    description="Rolls dice with any number of sides and reports the outcome.",
+    instruction="Use the roll_die tool to roll the dice the user asks for.",
+    tools=[roll_die],
+)
+
+# The whole agent becomes one MCP tool named "dice_agent".
+server = to_mcp_server(dice_agent)
+
+if __name__ == "__main__":
+  server.run(transport="stdio")
+```
+
+A host configured to launch this file sees one tool, `dice_agent`, and calls it
+with a `request` string; the ADK agent runs its own model and `roll_die` loop and
+returns the answer.
+
+## How it works
+
+`to_mcp_server` creates a `FastMCP` server and registers one tool whose handler
+runs the agent through a `Runner`. If no `runner` is supplied, one is built with
+in-memory session, artifact, memory, and credential services.
+
+On each tool call the handler:
+
+1.  Resolves an ADK session (see below), then wraps the incoming `request` string
+    as a user `Content`.
+2.  Drives `Runner.run_async` and iterates the event stream.
+3.  Forwards intermediate (non-final) text events to the host as MCP **progress
+    notifications**, so the host can show the agent working in real time.
+4.  Maps the parts of the final response to MCP content blocks and returns them:
+    text becomes `TextContent`, inline image data becomes `ImageContent`, audio
+    becomes `AudioContent`, and any other inline data becomes an
+    `EmbeddedResource`. This is why a multimodal agent's output is preserved
+    rather than flattened to text.
+
+### Session continuity
+
+`to_mcp_server` keeps one ADK session per MCP connection, so successive tool
+calls on the same connection form a single multi-turn conversation. The mapping
+from connection to session is held in a `weakref.WeakKeyDictionary`, so a
+session's entry is dropped when its connection is garbage-collected. Over stdio
+there is one connection per process, so all calls share one conversation; over
+streamable-http each client connection gets its own session.
+
+`to_mcp_server` depends on `Runner`, the agent (`BaseAgent`/`LlmAgent`),
+`google.genai.types`, and `mcp.server.fastmcp.FastMCP`; it returns a `FastMCP`
+that the caller runs on a transport of their choice.
+
+## Configuration options
+
+| Option | Type | Default | Description |
+| --- | --- | --- | --- |
+| `agent` | `BaseAgent` | *required* | The agent to serve. Its model loop and all of its tools are exposed together as one MCP tool. |
+| `name` | `str \| None` | `None` | The MCP server and tool name. Defaults to the agent's name (or `"adk_agent"`). Set it when you want the tool to appear under a name other than the agent's. |
+| `instructions` | `str \| None` | `None` | Optional server instructions an MCP host may surface to its model to describe how to use the tool. |
+| `runner` | `Runner \| None` | `None` | A pre-built `Runner`. If omitted, one is created with in-memory services. Supply your own to use persistent or custom session, artifact, memory, or credential services — this is the recommended path for a long-lived networked server. |
+
+## Advanced applications
+
+### Serving over the network
+
+*   **Problem solved**: a host on another machine needs to reach the agent.
+*   **Implementation**: run the same server with the networked transport:
+    `server.run(transport="streamable-http")`. Nothing about the agent changes;
+    only the transport differs.
+
+### Bringing your own services
+
+*   **Problem solved**: the default in-memory services do not persist across
+    process restarts and are not suited to multi-client production serving.
+*   **Implementation**: build a `Runner` with your chosen services and pass it in:
+    `to_mcp_server(agent, runner=my_runner)`. The tool then uses those services
+    for every call.
+
+### Multimodal responses
+
+*   **Problem solved**: the agent produces images or audio, not just text.
+*   **Implementation**: no extra work — non-text parts of the final response are
+    returned as `ImageContent`, `AudioContent`, or `EmbeddedResource`, so the
+    host receives them alongside any text.
+
+## Limitations
+
+*   **Text input only**: the tool accepts a single `request` string. Passing
+    media *into* the agent is not supported through the tool call, because MCP
+    tool arguments are JSON that the host's model fills in and hosts do not place
+    media in tool arguments. For media input, use MCP resources or elicitation
+    instead.
+*   **Default services are in-memory**: for a long-lived streamable-http server,
+    sessions accumulate with no eviction; inject a `runner` with a persistent or
+    cleaning session service. Tool calls on a single connection are expected to
+    be sequential, since they share one session.
+*   **Experimental**: `to_mcp_server` is `@experimental` and lives behind the
+    `mcp` extra; its behavior may change in future releases.
+
+## Related samples
+
+*   [MCP: serve an ADK agent](../../../../../contributing/samples/mcp/mcp_serve_agent)

src/google/adk/agents/llm_agent.py

@@ -147,7 +147,7 @@ async def _convert_tool_union_to_tools(
 
   # Wrap google_search tool with AgentTool if there are multiple tools because
   # the built-in tools cannot be used together with other tools.
-  # TODO(b/448114567): Remove once the workaround is no longer needed.
+  # TODO: Remove once the workaround is no longer needed.
   if multiple_tools and isinstance(tool_union, GoogleSearchTool):
     from ..tools.google_search_agent_tool import create_google_search_agent
     from ..tools.google_search_agent_tool import GoogleSearchAgentTool
@@ -159,7 +159,7 @@ async def _convert_tool_union_to_tools(
   # Replace VertexAiSearchTool with DiscoveryEngineSearchTool if there are
   # multiple tools because the built-in tools cannot be used together with
   # other tools.
-  # TODO(b/448114567): Remove once the workaround is no longer needed.
+  # TODO: Remove once the workaround is no longer needed.
   if multiple_tools and isinstance(tool_union, VertexAiSearchTool):
     from ..tools.discovery_engine_search_tool import DiscoveryEngineSearchTool
 
@@ -742,7 +742,7 @@ async def canonical_tools(
     """
     # We may need to wrap some built-in tools if there are other tools
     # because the built-in tools cannot be used together with other tools.
-    # TODO(b/448114567): Remove once the workaround is no longer needed.
+    # TODO: Remove once the workaround is no longer needed.
     multiple_tools = len(self.tools) > 1
     model = self.canonical_model
 

src/google/adk/agents/run_config.py

@@ -242,6 +242,9 @@ class RunConfig(BaseModel):
   realtime_input_config: Optional[types.RealtimeInputConfig] = None
   """Realtime input config for live agents with audio input from user."""
 
+  explicit_vad_signal: Optional[bool] = None
+  """Whether to enable explicit voice activity detection (VAD) signals from the model."""
+
   translation_config: Optional[types.TranslationConfig] = None
   """Configures real-time speech-to-speech translation.
 
@@ -379,6 +382,14 @@ class RunConfig(BaseModel):
   callers provide per-turn context without changing the conversation history.
   """
 
+  include_thoughts_from_other_agents: bool = False
+  """Whether to include other agents' thought parts in LLM context.
+
+  By default, thoughts from other agents are excluded when their messages are
+  reformatted as user context for the current agent. Enable this only when
+  agents are expected to share internal reasoning with one another.
+  """
+
   @model_validator(mode='before')
   @classmethod
   def check_for_deprecated_save_live_audio(cls, data: Any) -> Any:

src/google/adk/apps/llm_event_summarizer.py

@@ -50,7 +50,13 @@ class LlmEventSummarizer(BaseEventsSummarizer):
       ' It may or may not start from a compacted history. Please identify and'
       ' reiterate the user request, summarize the context so far, focusing on'
       ' key decisions made and information obtained, as well as any unresolved'
-      ' questions or tasks. The summary should be concise and capture the'
+      ' questions or tasks. '
+      'CRITICAL INSTRUCTIONS: '
+      '1. Explicitly identify and state the primary language used by the user '
+      'at the top of your summary (e.g., "Conversation Language: English"). '
+      '2. If the agent called any tools, accurately list the exact tool names '
+      'used to maintain tool grounding. '
+      'The rest of the summary should be concise and capture the'
       ' essence of the interaction.\n\n{conversation_history}'
   )
 

src/google/adk/cli/agent_graph.py

@@ -41,9 +41,9 @@
 async def build_graph(
     graph: graphviz.Digraph,
     agent: BaseAgent,
-    highlight_pairs,
-    parent_agent=None,
-):
+    highlight_pairs: list[tuple[str, str]] | None,
+    parent_agent: BaseAgent | None = None,
+) -> None:
   """
   Build a graph of the agent and its sub-agents.
   Args:
@@ -63,7 +63,7 @@ async def build_graph(
   light_gray = '#cccccc'
   white = '#ffffff'
 
-  def get_node_name(tool_or_agent: Union[BaseAgent, BaseTool]):
+  def get_node_name(tool_or_agent: Union[BaseAgent, BaseTool]) -> str:
     if isinstance(tool_or_agent, BaseAgent):
       # Added Workflow Agent checks for different agent types
       if isinstance(tool_or_agent, SequentialAgent):
@@ -81,7 +81,7 @@ def get_node_name(tool_or_agent: Union[BaseAgent, BaseTool]):
     else:
       raise ValueError(f'Unsupported tool type: {tool_or_agent}')
 
-  def get_node_caption(tool_or_agent: Union[BaseAgent, BaseTool]):
+  def get_node_caption(tool_or_agent: Union[BaseAgent, BaseTool]) -> str:
 
     if isinstance(tool_or_agent, BaseAgent):
       return '🤖 ' + tool_or_agent.name
@@ -105,7 +105,7 @@ def get_node_caption(tool_or_agent: Union[BaseAgent, BaseTool]):
       )
       return f'❓ Unsupported tool type: {type(tool_or_agent)}'
 
-  def get_node_shape(tool_or_agent: Union[BaseAgent, BaseTool]):
+  def get_node_shape(tool_or_agent: Union[BaseAgent, BaseTool]) -> str:
     if isinstance(tool_or_agent, BaseAgent):
       return 'ellipse'
     elif retrieval_tool_module_loaded and isinstance(
@@ -126,7 +126,9 @@ def get_node_shape(tool_or_agent: Union[BaseAgent, BaseTool]):
       )
       return 'cylinder'
 
-  def should_build_agent_cluster(tool_or_agent):
+  def should_build_agent_cluster(
+      tool_or_agent: Union[BaseAgent, BaseTool],
+  ) -> bool:
     if isinstance(tool_or_agent, Workflow):
       return True
     elif isinstance(tool_or_agent, BaseAgent):
@@ -149,7 +151,9 @@ def should_build_agent_cluster(tool_or_agent):
     else:
       return False
 
-  async def build_cluster(child: graphviz.Digraph, agent: BaseAgent, name: str):
+  async def build_cluster(
+      child: graphviz.Digraph, agent: BaseAgent, name: str
+  ) -> None:
     if isinstance(agent, LoopAgent):
       # Draw the edge from the parent agent to the first sub-agent
       if parent_agent:
@@ -215,7 +219,7 @@ async def build_cluster(child: graphviz.Digraph, agent: BaseAgent, name: str):
         fontcolor=light_gray,
     )
 
-  async def draw_node(tool_or_agent: Union[BaseAgent, BaseTool]):
+  async def draw_node(tool_or_agent: Union[BaseAgent, BaseTool]) -> None:
     name = get_node_name(tool_or_agent)
     shape = get_node_shape(tool_or_agent)
     caption = get_node_caption(tool_or_agent)
@@ -261,7 +265,7 @@ async def draw_node(tool_or_agent: Union[BaseAgent, BaseTool]):
 
       return
 
-  def draw_edge(from_name, to_name):
+  def draw_edge(from_name: str, to_name: str) -> None:
     if highlight_pairs:
       for highlight_from, highlight_to in highlight_pairs:
         if from_name == highlight_from and to_name == highlight_to:

src/google/adk/cli/agent_test_runner.py

@@ -370,7 +370,7 @@ def _normalize_ids(events: list[AdkEvent]) -> list[AdkEvent]:
             fc.args[k] = new_id
 
   # Pass 2: Update actions and user responses in all events
-  call_name_to_ids = {}
+  call_name_to_ids: dict[str | None, list[str | None]] = {}
   for e in events:
     for fc in e.get_function_calls():
       call_name_to_ids.setdefault(fc.name, []).append(fc.id)