feat: added python toolbox-langchain sdk docs

docs/en/sdks/python-sdk/langchain/index.md

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+---
+title: "Toolbox-langchain"
+type: docs
+weight: 8
+description: >
+  Toolbox-langchain SDK for connecting to the MCP Toolbox server and invoking tools programmatically.
+---
+
+## Overview
+
+The `toolbox-langchain` package provides a Python interface to the MCP Toolbox service, enabling you to load and invoke tools from your own applications.
+
+## Installation
+
+```bash
+pip install toolbox-langchain
+```
+## Quickstart
+
+Here's a minimal example to get you started using
+[LangGraph](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent):
+
+```py
+from toolbox_langchain import ToolboxClient
+from langchain_google_vertexai import ChatVertexAI
+from langgraph.prebuilt import create_react_agent
+
+async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
+    tools = toolbox.load_toolset()
+
+    model = ChatVertexAI(model="gemini-2.0-flash-001")
+    agent = create_react_agent(model, tools)
+
+    prompt = "How's the weather today?"
+
+    for s in agent.stream({"messages": [("user", prompt)]}, stream_mode="values"):
+        message = s["messages"][-1]
+        if isinstance(message, tuple):
+            print(message)
+        else:
+            message.pretty_print()
+```
+{{< notice tip >}}
+For a complete, end-to-end example including setting up the service and using an SDK, see the full tutorial: [Toolbox Quickstart Tutorial](getting-started/local_quickstart)
+{{< /notice >}}
+
+## Usage
+
+Import and initialize the toolbox client.
+
+```py
+from toolbox_langchain import ToolboxClient
+
+# Replace with your Toolbox service's URL
+async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
+```
+
+## Loading Tools
+
+### Load a toolset
+
+A toolset is a collection of related tools. You can load all tools in a toolset
+or a specific one:
+
+```py
+# Load all tools
+tools = toolbox.load_toolset()
+
+# Load a specific toolset
+tools = toolbox.load_toolset("my-toolset")
+```
+
+### Load a single tool
+
+```py
+tool = toolbox.load_tool("my-tool")
+```
+
+Loading individual tools gives you finer-grained control over which tools are
+available to your LLM agent.
+
+## Use with LangChain
+
+LangChain's agents can dynamically choose and execute tools based on the user
+input. Include tools loaded from the Toolbox SDK in the agent's toolkit:
+
+```py
+from langchain_google_vertexai import ChatVertexAI
+
+model = ChatVertexAI(model="gemini-2.0-flash-001")
+
+# Initialize agent with tools
+agent = model.bind_tools(tools)
+
+# Run the agent
+result = agent.invoke("Do something with the tools")
+```
+
+## Use with LangGraph
+
+Integrate the Toolbox SDK with LangGraph to use Toolbox service tools within a
+graph-based workflow. Follow the [official
+guide](https://langchain-ai.github.io/langgraph/) with minimal changes.
+
+### Represent Tools as Nodes
+
+Represent each tool as a LangGraph node, encapsulating the tool's execution within the node's functionality:
+
+```py
+from toolbox_langchain import ToolboxClient
+from langgraph.graph import StateGraph, MessagesState
+from langgraph.prebuilt import ToolNode
+
+# Define the function that calls the model
+def call_model(state: MessagesState):
+    messages = state['messages']
+    response = model.invoke(messages)
+    return {"messages": [response]}  # Return a list to add to existing messages
+
+model = ChatVertexAI(model="gemini-2.0-flash-001")
+builder = StateGraph(MessagesState)
+tool_node = ToolNode(tools)
+
+builder.add_node("agent", call_model)
+builder.add_node("tools", tool_node)
+```
+
+### Connect Tools with LLM
+
+Connect tool nodes with LLM nodes. The LLM decides which tool to use based on
+input or context. Tool output can be fed back into the LLM:
+
+```py
+from typing import Literal
+from langgraph.graph import END, START
+from langchain_core.messages import HumanMessage
+
+# Define the function that determines whether to continue or not
+def should_continue(state: MessagesState) -> Literal["tools", END]:
+    messages = state['messages']
+    last_message = messages[-1]
+    if last_message.tool_calls:
+        return "tools"  # Route to "tools" node if LLM makes a tool call
+    return END  # Otherwise, stop
+
+builder.add_edge(START, "agent")
+builder.add_conditional_edges("agent", should_continue)
+builder.add_edge("tools", 'agent')
+
+graph = builder.compile()
+
+graph.invoke({"messages": [HumanMessage(content="Do something with the tools")]})
+```
+
+## Manual usage
+
+Execute a tool manually using the `invoke` method:
+
+```py
+result = tools[0].invoke({"name": "Alice", "age": 30})
+```
+
+This is useful for testing tools or when you need precise control over tool
+execution outside of an agent framework.
+
+## Client to Server Authentication
+
+This section describes how to authenticate the ToolboxClient itself when
+connecting to a Toolbox server instance that requires authentication. This is
+crucial for securing your Toolbox server endpoint, especially when deployed on
+platforms like Cloud Run, GKE,  or any environment where unauthenticated access
+is restricted.
+
+This client-to-server authentication ensures that the Toolbox server can verify
+the identity of the client making the request before any tool is loaded or
+called. It is different from [Authenticating Tools](#authenticating-tools),
+which deals with providing credentials for specific tools within an already
+connected Toolbox session.
+
+### When is Client-to-Server Authentication Needed?
+
+You'll need this type of authentication if your Toolbox server is configured to
+deny unauthenticated requests. For example:
+
+- Your Toolbox server is deployed on Cloud Run and configured to "Require authentication."
+- Your server is behind an Identity-Aware Proxy (IAP) or a similar
+  authentication layer.
+- You have custom authentication middleware on your self-hosted Toolbox server.
+
+Without proper client authentication in these scenarios, attempts to connect or
+make calls (like `load_tool`) will likely fail with `Unauthorized` errors.
+
+### How it works
+
+The `ToolboxClient` allows you to specify functions (or coroutines for the async
+client) that dynamically generate HTTP headers for every request sent to the
+Toolbox server. The most common use case is to add an Authorization header with
+a bearer token (e.g., a Google ID token).
+
+These header-generating functions are called just before each request, ensuring
+that fresh credentials or header values can be used.
+
+### Configuration
+
+You can configure these dynamic headers as follows:
+
+```python
+from toolbox_langchain import ToolboxClient
+
+async with ToolboxClient(
+    "toolbox-url", 
+    client_headers={"header1": header1_getter, "header2": header2_getter, ...}
+) as client:
+```
+
+### Authenticating with Google Cloud Servers
+
+For Toolbox servers hosted on Google Cloud (e.g., Cloud Run) and requiring
+`Google ID token` authentication, the helper module
+[auth_methods](https://github.com/googleapis/mcp-toolbox-sdk-python/blob/main/packages/toolbox-core/src/toolbox_core/auth_methods.py) provides utility functions.
+
+### Step by Step Guide for Cloud Run
+
+1. **Configure Permissions**:
+   [Grant](https://cloud.google.com/run/docs/securing/managing-access#service-add-principals)
+   the `roles/run.invoker` IAM role on the Cloud
+   Run service to the principal. This could be your `user account email` or a
+   `service account`.
+2. **Configure Credentials**
+    - Local Development: Set up
+   [ADC](https://cloud.google.com/docs/authentication/set-up-adc-local-dev-environment).
+    - Google Cloud Environments: When running within Google Cloud (e.g., Compute
+      Engine, GKE, another Cloud Run service, Cloud Functions), ADC is typically
+      configured automatically, using the environment's default service account.
+3. **Connect to the Toolbox Server**
+
+    ```python
+    from toolbox_langchain import ToolboxClient
+    from toolbox_core import auth_methods
+
+    auth_token_provider = auth_methods.aget_google_id_token(URL) # can also use sync method
+    async with ToolboxClient(
+        URL,
+        client_headers={"Authorization": auth_token_provider},
+    ) as client:
+        tools = client.load_toolset()
+
+        # Now, you can use the client as usual.
+    ```
+
+
+## Authenticating Tools
+
+{{< notice info >}}
+Always use HTTPS to connect your application with the Toolbox service, especially when using tools with authentication configured. Using HTTP exposes your application to serious security risks.
+{{< /notice >}}
+
+Some tools require user authentication to access sensitive data.
+
+### Supported Authentication Mechanisms
+Toolbox currently supports authentication using the [OIDC
+protocol](https://openid.net/specs/openid-connect-core-1_0.html) with [ID
+tokens](https://openid.net/specs/openid-connect-core-1_0.html#IDToken) (not
+access tokens) for [Google OAuth
+2.0](https://cloud.google.com/apigee/docs/api-platform/security/oauth/oauth-home).
+
+### Configure Tools
+
+Refer to [these
+instructions](https://googleapis.github.io/genai-toolbox/resources/tools/#authenticated-parameters) on
+configuring tools for authenticated parameters.
+
+### Configure SDK
+
+You need a method to retrieve an ID token from your authentication service:
+
+```py
+async def get_auth_token():
+    # ... Logic to retrieve ID token (e.g., from local storage, OAuth flow)
+    # This example just returns a placeholder. Replace with your actual token retrieval.
+    return "YOUR_ID_TOKEN" # Placeholder
+```
+
+#### Add Authentication to a Tool
+
+```py
+async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
+    tools = toolbox.load_toolset()
+
+    auth_tool = tools[0].add_auth_token_getter("my_auth", get_auth_token) # Single token
+
+    multi_auth_tool = tools[0].add_auth_token_getters({"auth_1": get_auth_1}, {"auth_2": get_auth_2}) # Multiple tokens
+
+    # OR
+
+    auth_tools = [tool.add_auth_token_getter("my_auth", get_auth_token) for tool in tools]
+```
+
+#### Add Authentication While Loading
+
+```py
+auth_tool = toolbox.load_tool(auth_token_getters={"my_auth": get_auth_token})
+
+auth_tools = toolbox.load_toolset(auth_token_getters={"my_auth": get_auth_token})
+```
+{{< notice note >}}
+Adding auth tokens during loading only affect the tools loaded within that call.
+{{< /notice >}}
+
+### Complete Example
+
+```py
+import asyncio
+from toolbox_langchain import ToolboxClient
+
+async def get_auth_token():
+    # ... Logic to retrieve ID token (e.g., from local storage, OAuth flow)
+    # This example just returns a placeholder. Replace with your actual token retrieval.
+    return "YOUR_ID_TOKEN" # Placeholder
+
+async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
+    tool = toolbox.load_tool("my-tool")
+
+    auth_tool = tool.add_auth_token_getter("my_auth", get_auth_token)
+    result = auth_tool.invoke({"input": "some input"})
+    print(result)
+```
+
+## Binding Parameter Values
+
+Predetermine values for tool parameters using the SDK. These values won't be
+modified by the LLM. This is useful for:
+
+* **Protecting sensitive information:**  API keys, secrets, etc.
+* **Enforcing consistency:** Ensuring specific values for certain parameters.
+* **Pre-filling known data:**  Providing defaults or context.
+
+### Binding Parameters to a Tool
+
+```py
+async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
+    tools = toolbox.load_toolset()
+
+    bound_tool = tool[0].bind_param("param", "value") # Single param
+
+    multi_bound_tool = tools[0].bind_params({"param1": "value1", "param2": "value2"}) # Multiple params
+
+    # OR
+
+    bound_tools = [tool.bind_param("param", "value") for tool in tools]
+```
+
+### Binding Parameters While Loading
+
+```py
+bound_tool = toolbox.load_tool("my-tool", bound_params={"param": "value"})
+
+bound_tools = toolbox.load_toolset(bound_params={"param": "value"})
+```
+{{< notice note >}}
+Bound values during loading only affect the tools loaded in that call.
+{{< /notice >}}
+
+### Binding Dynamic Values
+
+Use a function to bind dynamic values:
+
+```py
+def get_dynamic_value():
+  # Logic to determine the value
+  return "dynamic_value"
+
+dynamic_bound_tool = tool.bind_param("param", get_dynamic_value)
+```
+{{< notice note >}}
+You don’t need to modify tool configurations to bind parameter values.
+{{< /notice >}}
+
+## Asynchronous Usage
+
+For better performance through [cooperative
+multitasking](https://en.wikipedia.org/wiki/Cooperative_multitasking), you can
+use the asynchronous interfaces of the `ToolboxClient`.
+
+{{< notice note >}}
+Asynchronous interfaces like `aload_tool` and `aload_toolset` require an asynchronous environment. For guidance on running asynchronous Python programs, see [asyncio documentation](https://docs.python.org/3/library/asyncio-runner.html#running-an-asyncio-program).
+{{< /notice >}}
+
+```py
+import asyncio
+from toolbox_langchain import ToolboxClient
+
+async def main():
+    async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
+        tool = await client.aload_tool("my-tool")
+        tools = await client.aload_toolset()
+        response = await tool.ainvoke()
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```