Found docs updates needed from ADK python release v1.16.0 to v1.17.0

Between ADK Python releases v1.16.0 and v1.17.0, several new features, enhancements, and bug fixes have been introduced that require documentation updates.

You can see the full list of changes in the release comparison: https://github.com/google/adk-python/compare/v1.16.0...v1.17.0

Here are the detailed instructions for updating the ADK documentation:

### New Features

1. **✨ New `AgentEngineSandboxCodeExecutor` for executing code in a sandbox.**
A new code executor, `AgentEngineSandboxCodeExecutor`, has been added to execute code using the Agent Engine Code Execution Sandbox. A new documentation section should be added to explain this feature.

**Proposed Change**:
In `docs/tools/built-in-tools.md`, add a new section for the `AgentEngineSandboxCodeExecutor`.

```markdown
### Agent Engine Sandbox Code Executor

The `AgentEngineSandboxCodeExecutor` provides a secure way to execute LLM-generated code within the Agent Engine Code Execution Sandbox. This is particularly useful for data analysis tasks.

#### How to use

1. Follow the instructions at https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/code-execution/overview to create a code execution sandbox environment.
2. Replace `SANDBOX_RESOURCE_NAME` with the one you just created.

**Example:**
```python
from google.adk.agents.llm_agent import Agent
from google.adk.code_executors.agent_engine_sandbox_code_executor import AgentEngineSandboxCodeExecutor

root_agent = Agent(
model="gemini-2.0-flash-001",
name="agent_engine_code_execution_agent",
instruction="",
code_executor=AgentEngineSandboxCodeExecutor(
sandbox_resource_name="SANDBOX_RESOURCE_NAME",
agent_engine_resource_name="AGENT_ENGINE_RESOURCE_NAME",
),
)
```
For a complete example, see the [agent_engine_code_execution sample](https://github.com/google/adk-python/tree/main/contributing/samples/agent_engine_code_execution).
```

**Reasoning**:
This is a new feature that provides a secure way to execute code and needs to be documented for users.

**Reference**:
- `contributing/samples/agent_engine_code_execution/agent.py`

2. **✨ New `ComputerUseToolset` for browser automation.**
The `ComputerUseToolset` is a new toolset that allows agents to control a browser using Playwright to complete user tasks. A new documentation page should be created for this toolset.

**Proposed Change**:
Create a new file `docs/tools/computer-use-toolset.md` with the following content:

```markdown
# Computer Use Toolset

The `ComputerUseToolset` allows an agent to operate a browser to complete user tasks. It uses Playwright to control a Chromium browser and can interact with web pages by taking screenshots, clicking, typing, and navigating.

## Setup

1. **Install Python Dependencies**:
```bash
uv pip install -r internal/samples/computer_use/requirements.txt
```
2. **Install Playwright Dependencies**:
```bash
playwright install-deps chromium
```
3. **Install Chromium Browser**:
```bash
playwright install chromium
```

## Usage

To use the `ComputerUseToolset`, you need to provide a `Computer` implementation. The `PlaywrightComputer` is provided for this purpose.

**Example:**
```python
from google.adk import Agent
from google.adk.tools.computer_use.computer_use_toolset import ComputerUseToolset
from .playwright import PlaywrightComputer

root_agent = Agent(
model='gemini-2.5-computer-use-preview-10-2025',
name='computer_use_agent',
description='A computer use agent that can operate a browser to finish user tasks.',
instruction="you are a computer use agent",
tools=[
ComputerUseToolset(computer=PlaywrightComputer(screen_size=(1280, 936)))
],
)
```
For a complete example, see the [computer_use sample](https://github.com/google/adk-python/tree/main/contributing/samples/computer_use).
```

**Reasoning**:
This is a major new feature that enables a whole new class of browser automation agents.

**Reference**:
- `contributing/samples/computer_use/agent.py`
- `contributing/samples/computer_use/playwright.py`

3. **✨ New `ReflectAndRetryToolPlugin` for error recovery.**
The `ReflectAndRetryToolPlugin` provides a self-healing mechanism for tool failures by reflecting on errors and retrying with different arguments. A new documentation page should be created for this plugin.

**Proposed Change**:
Create a new file `docs/plugins/reflect-and-retry.md` with the following content:

```markdown
# Reflect and Retry Tool Plugin

The `ReflectAndRetryToolPlugin` provides self-healing, concurrent-safe error recovery for tool failures.

**Key Features:**
- **Concurrency Safe**: Uses locking to safely handle parallel tool executions.
- **Configurable Scope**: Tracks failures per-invocation (default) or globally.
- **Granular Tracking**: Failure counts are tracked per-tool.
- **Custom Error Extraction**: Supports detecting errors in normal tool responses.

## Basic Usage

The following example shows how to use the plugin to retry a tool that can fail.

```python
from google.adk.apps.app import App
from google.adk.plugins import ReflectAndRetryToolPlugin

app = App(
name="my_app",
root_agent=root_agent,
plugins=[
ReflectAndRetryToolPlugin(
max_retries=6, throw_exception_if_retry_exceeded=False
),
],
)
```
For a complete example, see the [plugin_reflect_tool_retry sample](https://github.com/google/adk-python/tree/main/contributing/samples/plugin_reflect_tool_retry).
```

**Reasoning**:
This is a useful new plugin that improves the reliability of agents.

**Reference**:
- `contributing/samples/plugin_reflect_tool_retry/basic/agent.py`

4. **✨ New `rewind_session` feature.**
A new feature has been added to allow rewinding a session to a previous state. This should be documented in the sessions documentation.

**Proposed Change**:
In `docs/sessions/index.md` (or a more appropriate page), add a new section:

```markdown
## Rewinding a Session

You can rewind a session to a previous state using the `runner.rewind_async` method. This is useful for debugging or re-running parts of a conversation.

**Example:**
```python
await runner.rewind_async(
user_id="test_user",
session_id="test_session",
rewind_before_invocation_id=""
)
```
For a complete example, see the [rewind_session sample](https://github.com/google/adk-python/tree/main/contributing/samples/rewind_session).
```

**Reasoning**:
This is a new and powerful feature for session management that needs to be documented.

**Reference**:
- `contributing/samples/rewind_session/agent.py`

### Feature Updates

5. **🚀 `McpToolset` now supports dynamic headers.**
The `McpToolset` now accepts a `header_provider` to dynamically generate headers for each MCP request.

**Proposed Change**:
In `docs/tools/mcp-tools.md`, add a subsection explaining the `header_provider`.

**Current state**:
The documentation for `McpToolset` does not mention `header_provider`.

**Proposed Change**:
```markdown
### Dynamic Headers with `header_provider`

You can provide a `header_provider` to the `McpToolset` to dynamically generate headers for each request. This is useful for multi-tenancy or other scenarios where headers need to change based on the context.

**Example:**
```python
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import StreamableHTTPConnectionParams

root_agent = LlmAgent(
model='gemini-2.0-flash',
name='tenant_agent',
instruction="...",
tools=[
McpToolset(
connection_params=StreamableHTTPConnectionParams(
url='http://localhost:3000/mcp',
),
tool_filter=['get_tenant_data'],
header_provider=lambda ctx: {'X-Tenant-ID': 'tenant1'},
)
],
)
```
For a complete example, see the [mcp_dynamic_header_agent sample](https://github.com/google/adk-python/tree/main/contributing/samples/mcp_dynamic_header_agent).
```

**Reasoning**:
This is a new feature for `McpToolset` that enhances its capabilities.

**Reference**:
- `contributing/samples/mcp_dynamic_header_agent/agent.py`

6. **🚀 `GoogleSearchTool` and `VertexAiSearchTool` can now be used with other tools.**
The `GoogleSearchTool` and `VertexAiSearchTool` now have a `bypass_multi_tools_limit` parameter that allows them to be used with other tools in the same agent.

**Proposed Change**:
In `docs/tools/built-in-tools.md`, update the "Use Built-in tools with other tools" section and the "Limitations" section.

**Current state**:
The documentation states that built-in tools cannot be used with other tools in the same agent.

**Proposed Change**:
Update the documentation to explain the `bypass_multi_tools_limit` parameter and provide an example.

```markdown
### Use Built-in tools with other tools

You can now use `GoogleSearchTool` and `VertexAiSearchTool` with other tools by setting `bypass_multi_tools_limit=True`.

**Example:**
```python
from google.adk.tools.google_search_tool import GoogleSearchTool
from google.adk.tools.vertex_ai_search_tool import VertexAiSearchTool

root_agent = Agent(
name="MyAgent",
model="gemini-2.5-flash",
tools=[
my_custom_tool,
GoogleSearchTool(bypass_multi_tools_limit=True),
VertexAiSearchTool(data_store_id="my-data-store", bypass_multi_tools_limit=True),
],
)
```

The limitation about using only one built-in tool should be updated to reflect this change.
```

**Reasoning**:
This is an important update that removes a previous limitation of built-in tools.

**Reference**:
- `contributing/samples/built_in_multi_tools/agent.py`

7. **🚀 `LlmAgent.static_instruction` now supports `types.ContentUnion`.**
The `static_instruction` parameter of `LlmAgent` now accepts a wider range of types.

**Proposed Change**:
In `docs/agents/llm-agents.md`, update the description of `static_instruction`.

**Current state**:
The documentation may not fully describe the supported types for `static_instruction`.

**Proposed Change**:
```markdown
**`static_instruction` (Optional[types.ContentUnion]):** Static instruction content sent literally as system instruction.
...
**Content Support:**
Accepts `types.ContentUnion` which includes:
- `str`: Simple text instruction
- `types.Content`: Rich content object
- `types.Part`: Single part (text, inline_data, file_data, etc.)
- `PIL.Image.Image`: Image object
- `types.File`: File reference
- `list[PartUnion]`: List of parts
```

**Reasoning**:
To accurately reflect the enhanced capabilities of the `static_instruction` parameter.

**Reference**:
- `src/google/adk/agents/llm_agent.py`

8. **🚀 `RunConfig` now supports `context_window_compression`.**
A new attribute `context_window_compression` has been added to `RunConfig`.

**Proposed Change**:
In `docs/runtime/runconfig.md`, add the new attribute to the table and provide a description.

**Current state**:
`context_window_compression` is not documented.

**Proposed Change**:
```markdown
| `context_window_compression` | `Optional[types.ContextWindowCompressionConfig]` | | `None` | Configuration for context window compression. |
```

**Reasoning**:
To document the new `context_window_compression` feature.

**Reference**:
- `src/google/adk/agents/run_config.py`

9. **🚀 Artifact Service has new methods.**
The `BaseArtifactService` now includes `list_artifact_versions` and `get_artifact_version` methods.

**Proposed Change**:
In `docs/artifacts/index.md`, update the "Artifact Service (`BaseArtifactService`)" section.

**Current state**:
The documentation for `BaseArtifactService` is missing the new methods.

**Proposed Change**:
```markdown
* `list_artifact_versions`: Lists all versions and their metadata for a specific artifact.
* `get_artifact_version`: Gets the metadata for a specific version of an artifact.
```

**Reasoning**:
To keep the API documentation for `BaseArtifactService` up to date.

**Reference**:
- `src/google/adk/artifacts/base_artifact_service.py`

10. **📝 Docstrings added to LangChain tools.**
The docstrings in the `langchain_structured_tool_agent` sample have been updated.

**Proposed Change**:
In `docs/tools/third-party/index.md`, ensure the example code for LangChain tools includes the new docstrings.

**Reasoning**:
To provide better documentation and examples for using LangChain tools.

**Reference**:
- `contributing/samples/langchain_structured_tool_agent/agent.py`

11. **⚙️ Model name updates.**
Numerous occurrences of `gemini-2.0-flash` should be updated to `gemini-2.5-flash`.

**Proposed Change**:
Globally replace `gemini-2.0-flash` with `gemini-2.5-flash` in the documentation where appropriate, to reflect the use of the newer model.

**Reasoning**:
To keep the documentation current with the recommended models.

**Reference**:
- `llms-full.txt` (and many other files)