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+---
+title: "Core"
+type: docs
+weight: 8
+description: >
+  MCP Toolbox Core SDK for integrating functionalities of MCP Toolbox into your Agentic apps.
+---
+
+## Overview
+
+The `toolbox-core` 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-core
+```
+
+{{< notice note >}}
+* The primary `ToolboxClient` is asynchronous and requires using `await` for loading and invoking tools, as shown in most examples.
+*  Asynchronous code needs to run within an event loop (e.g., using `asyncio.run()` or in an async framework). See the [Python `asyncio` documentation](https://docs.python.org/3/library/asyncio-task.html) for more details.
+* If you prefer synchronous execution, refer to the [Synchronous Usage](#synchronous-usage) section below.
+{{< /notice >}}
+
+{{< notice note>}}
+The `ToolboxClient` (and its synchronous counterpart `ToolboxSyncClient`) interacts with network resources using an underlying HTTP client session. You should remember to use a context manager or explicitly call `close()` to clean up these resources. If you provide your own session, you'll need to close it in addition to calling `ToolboxClient.close()`.
+{{< /notice >}}
+
+## Quickstart
+
+1. **Start the Toolbox Service**
+   - Make sure the MCP Toolbox service is running on port `5000` of your local machine. See the [Toolbox Getting Started Guide](/getting-started/introduction/#getting-started).
+
+2. **Minimal Example**
+
+```python
+import asyncio
+from toolbox_core import ToolboxClient
+
+async def main():
+    # Replace with the actual URL where your Toolbox service is running
+    async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
+        weather_tool = await toolbox.load_tool("get_weather")
+        result = await weather_tool(location="London")
+        print(result)
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+
+{{< 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**](https://googleapis.github.io/genai-toolbox/getting-started/local_quickstart)
+{{< /notice >}}
+
+{{< notice note>}}
+If you initialize `ToolboxClient` without providing an external session and cannot use `async with`, you must explicitly close the client using `await toolbox.close()` in a `finally` block. This ensures the internally created session is closed.
+
+  ```py
+  toolbox = ToolboxClient("http://127.0.0.1:5000")
+  try:
+      # ... use toolbox ...
+  finally:
+      await toolbox.close()
+  ```
+{{< /notice >}}
+
+## Usage
+
+Import and initialize an MCP Toolbox client, pointing it to the URL of your running
+Toolbox service.
+
+```py
+from toolbox_core import ToolboxClient
+
+# Replace with your Toolbox service's URL
+async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
+```
+
+All interactions for loading and invoking tools happen through this client.
+
+{{< notice tip>}}
+For advanced use cases, you can provide an external `aiohttp.ClientSession` during initialization (e.g., `ToolboxClient(url, session=my_session)`). If you provide your own session, you are responsible for managing its lifecycle; `ToolboxClient` *will not* close it.
+{{< /notice >}}
+
+{{< notice note>}}
+Closing the `ToolboxClient` also closes the underlying network session shared by all tools loaded from that client. As a result, any tool instances you have loaded will cease to function and will raise an error if you attempt to invoke them after the client is closed.
+{{< /notice >}}
+
+## Transport Protocols
+
+The SDK supports multiple transport protocols for communicating with the Toolbox server. By default, the client uses the latest supported version of the **Model Context Protocol (MCP)**.
+
+You can explicitly select a protocol using the `protocol` option during client initialization. This is useful if you need to use the native Toolbox HTTP protocol or pin the client to a specific legacy version of MCP.
+
+{{< notice note >}}
+* **Native Toolbox Transport**: This uses the service's native **REST over HTTP** API.
+* **MCP Transports**: These options use the **Model Context Protocol over HTTP**.
+{{< /notice >}}
+
+### Supported Protocols
+
+| Constant | Description |
+| :--- | :--- |
+| `Protocol.MCP` | **(Default)** Alias for the default MCP version (currently `2025-06-18`). |
+| `Protocol.TOOLBOX` | **DEPRECATED**: The native Toolbox HTTP protocol. Will be removed on March 4, 2026. |
+| `Protocol.MCP_v20251125` | MCP Protocol version 2025-11-25. |
+| `Protocol.MCP_v20250618` | MCP Protocol version 2025-06-18. |
+| `Protocol.MCP_v20241105` | MCP Protocol version 2024-11-05. |
+
+{{< notice note >}}
+The **Native Toolbox Protocol** (`Protocol.TOOLBOX`) is deprecated and will be removed on **March 4, 2026**.
+Please migrate to using the **MCP Protocol** (`Protocol.MCP`), which is the default.
+{{< /notice >}}
+
+### Example
+
+If you wish to use the native Toolbox protocol:
+
+```py
+from toolbox_core import ToolboxClient
+from toolbox_core.protocol import Protocol
+
+async with ToolboxClient("http://127.0.0.1:5000", protocol=Protocol.TOOLBOX) as toolbox:
+    # Use client
+    pass
+```
+
+If you want to pin the MCP Version 2025-03-26:
+
+```py
+from toolbox_core import ToolboxClient
+from toolbox_core.protocol import Protocol
+
+async with ToolboxClient("http://127.0.0.1:5000", protocol=Protocol.MCP_v20250326) as toolbox:
+    # Use client
+    pass
+```
+
+## Loading Tools
+
+You can load tools individually or in groups (toolsets) as defined in your
+Toolbox service configuration. Loading a toolset is convenient when working with
+multiple related functions, while loading a single tool offers more granular
+control.
+
+### 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 = await toolbox.load_toolset()
+
+# Load a specific toolset
+tools = await toolbox.load_toolset("my-toolset")
+```
+
+### Load a single tool
+
+Loads a specific tool by its unique name. This provides fine-grained control.
+
+```py
+tool = await toolbox.load_tool("my-tool")
+```
+
+## Invoking Tools
+
+Once loaded, tools behave like awaitable Python functions. You invoke them using
+`await` and pass arguments corresponding to the parameters defined in the tool's
+configuration within the Toolbox service.
+
+```py
+tool = await toolbox.load_tool("my-tool")
+result = await tool("foo", bar="baz")
+```
+
+{{< notice tip>}}
+For a more comprehensive guide on setting up the Toolbox service itself, which you'll need running to use this SDK, please refer to the [Toolbox Quickstart Guide](getting-started/local_quickstart).
+{{< /notice >}}
+
+## Synchronous Usage
+
+By default, the `ToolboxClient` and the `ToolboxTool` objects it produces behave like asynchronous Python functions, requiring the use of `await`.
+
+If your application primarily uses synchronous code, or you prefer not to manage an asyncio event loop, you can use the synchronous alternatives provided:
+
+* `ToolboxSyncClient`: The synchronous counterpart to `ToolboxClient`.
+* `ToolboxSyncTool`: The synchronous counterpart to `ToolboxTool`.
+
+The `ToolboxSyncClient` handles communication with the Toolbox service synchronously and produces `ToolboxSyncTool` instances when you load tools. You do not use the `await` keyword when interacting with these synchronous versions.
+
+```py
+from toolbox_core import ToolboxSyncClient
+
+with ToolboxSyncClient("http://127.0.0.1:5000") as toolbox:
+    weather_tool = toolbox.load_tool("get_weather")
+    result = weather_tool(location="Paris")
+    print(result)
+```
+
+{{< notice tip>}}
+While synchronous invocation is available for convenience, it's generally considered best practice to use asynchronous operations (like those provided by the default `ToolboxClient` and `ToolboxTool`) for an I/O-bound task like tool invocation. Asynchronous programming allows for cooperative multitasking, often leading to better performance and resource utilization, especially in applications handling concurrent requests.
+{{< /notice >}}
+
+## Use with LangGraph
+
+The Toolbox Core SDK integrates smoothly with frameworks like LangGraph,
+allowing you to incorporate tools managed by the Toolbox service into your
+agentic workflows.
+
+{{< notice tip>}}
+The loaded tools (both async `ToolboxTool` and sync `ToolboxSyncTool`) are callable and can often be used directly. However, to ensure parameter descriptions from Google-style docstrings are accurately parsed and made available to the LLM (via `bind_tools()`) and LangGraph internals, it's recommended to wrap the loaded tools using LangChain's [`StructuredTool`](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.structured.StructuredTool.html).
+{{< /notice >}}
+
+
+Here's a conceptual example adapting the [official LangGraph tool calling
+guide](https://langchain-ai.github.io/langgraph/how-tos/tool-calling):
+
+```py
+import asyncio
+from typing import Annotated
+from typing_extensions import TypedDict
+from langchain_core.messages import HumanMessage, BaseMessage
+from toolbox_core import ToolboxClient
+from langchain_google_vertexai import ChatVertexAI
+from langgraph.graph import StateGraph, START, END
+from langgraph.prebuilt import ToolNode
+from langchain.tools import StructuredTool
+from langgraph.graph.message import add_messages
+
+class State(TypedDict):
+    messages: Annotated[list[BaseMessage], add_messages]
+
+async def main():
+    async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
+        tools = await toolbox.load_toolset()
+        wrapped_tools = [StructuredTool.from_function(tool, parse_docstring=True) for tool in tools]
+        model_with_tools = ChatVertexAI(model="gemini-3-flash-preview").bind_tools(wrapped_tools)
+        tool_node = ToolNode(wrapped_tools)
+
+        def call_agent(state: State):
+            response = model_with_tools.invoke(state["messages"])
+            return {"messages": [response]}
+
+        def should_continue(state: State):
+            last_message = state["messages"][-1]
+            if last_message.tool_calls:
+                return "tools"
+            return END
+
+        graph_builder = StateGraph(State)
+
+        graph_builder.add_node("agent", call_agent)
+        graph_builder.add_node("tools", tool_node)
+
+        graph_builder.add_edge(START, "agent")
+        graph_builder.add_conditional_edges(
+            "agent",
+            should_continue,
+        )
+        graph_builder.add_edge("tools", "agent")
+
+        app = graph_builder.compile()
+
+        prompt = "What is the weather in London?"
+        inputs = {"messages": [HumanMessage(content=prompt)]}
+
+        print(f"User: {prompt}\n")
+        print("--- Streaming Agent Steps ---")
+
+        events = app.stream(
+            inputs,
+            stream_mode="values",
+        )
+
+        for event in events:
+            event["messages"][-1].pretty_print()
+            print("\n---\n")
+
+asyncio.run(main())
+```
+
+## Client to Server Authentication
+
+This section describes how to authenticate the ToolboxClient itself when
+connecting to an MCP 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` (and `ToolboxSyncClient`) 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 seen below:
+
+```python
+from toolbox_core import ToolboxClient
+
+async with ToolboxClient("toolbox-url", client_headers={"header1": header1_getter, "header2": header2_getter, ...}) as client:
+    # Use client
+    pass
+```
+
+### 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_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 = await 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 in **production environments** or whenever the communication involves **sensitive data** (including scenarios where tools require authentication tokens). Using plain HTTP lacks encryption and exposes your application and data to significant security risks, such as eavesdropping and tampering.
+{{</notice>}}
+
+Tools can be configured within the Toolbox service to require authentication,
+ensuring only authorized users or applications can invoke them, especially when
+accessing sensitive data.
+
+### When is Authentication Needed?
+
+Authentication is configured per-tool within the Toolbox service itself. If a
+tool you intend to use is marked as requiring authentication in the service, you
+must configure the SDK client to provide the necessary credentials (currently
+Oauth2 tokens) when invoking that specific tool.
+
+### Supported Authentication Mechanisms
+
+The Toolbox service enables secure tool usage through **Authenticated Parameters**. For detailed information on how these mechanisms work within the Toolbox service and how to configure them, please refer to [Authenticated Parameters](resources/tools/#authenticated-parameters)
+
+### Step 1: Configure Tools in Toolbox Service
+
+First, ensure the target tool(s) are configured correctly in the Toolbox service
+to require authentication. Refer to the [Authenticated Parameters](https://googleapis.github.io/genai-toolbox/resources/tools/#authenticated-parameters)
+for instructions.
+
+### Step 2: Configure SDK Client
+
+Your application needs a way to obtain the required Oauth2 token for the
+authenticated user. The SDK requires you to provide a function capable of
+retrieving this token *when the tool is invoked*.
+
+#### Provide an ID Token Retriever Function
+
+You must provide the SDK with a function (sync or async) that returns the
+necessary token when called. The implementation depends on your application's
+authentication flow (e.g., retrieving a stored token, initiating an OAuth flow).
+
+{{< notice info>}}
+The name used when registering the getter function with the SDK (e.g.,`"my_api_token"`) must exactly match the `name` of the corresponding `authServices` defined in the tool's configuration within the Toolbox service.
+{{</notice>}}
+
+```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
+```
+
+{{< notice tip>}}
+Your token retriever function is invoked every time an authenticated parameter requires a token for a tool call. Consider implementing caching logic within this function to avoid redundant token fetching or generation, especially for tokens with longer validity periods or if the retrieval process is resource-intensive.
+{{</notice>}}
+
+#### Option A: Add Authentication to a Loaded Tool
+
+You can add the token retriever function to a tool object *after* it has been
+loaded. This modifies the specific tool instance.
+
+```py
+async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
+    tool = await toolbox.load_tool("my-tool")
+
+    auth_tool = tool.add_auth_token_getter("my_auth", get_auth_token)  # Single token
+
+    # OR
+
+    multi_auth_tool = tool.add_auth_token_getters({
+        "my_auth_1": get_auth_token_1,
+        "my_auth_2": get_auth_token_2,
+    })  # Multiple tokens
+```
+
+#### Option B: Add Authentication While Loading Tools
+
+You can provide the token retriever(s) directly during the `load_tool` or
+`load_toolset` calls. This applies the authentication configuration only to the
+tools loaded in that specific call, without modifying the original tool objects
+if they were loaded previously.
+
+```py
+auth_tool = await toolbox.load_tool(auth_token_getters={"my_auth": get_auth_token})
+
+# OR
+
+auth_tools = await toolbox.load_toolset(auth_token_getters={"my_auth": get_auth_token})
+```
+
+{{< notice >}}
+Adding auth tokens during loading only affect the tools loaded within that call.
+{{</notice>}}
+
+### Complete Authentication Example
+
+```py
+import asyncio
+from toolbox_core 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 = await toolbox.load_tool("my-tool")
+
+    auth_tool = tool.add_auth_token_getters({"my_auth": get_auth_token})
+    result = auth_tool(input="some input")
+    print(result)
+```
+
+{{< notice >}}
+An auth token getter for a specific name (e.g., `“GOOGLE_ID”`) will replace any client header with the same name followed by `“_token”` (e.g., `“GOOGLE_ID_token”`).
+{{</notice>}}
+
+
+## Parameter Binding
+
+The SDK allows you to pre-set, or "bind", values for specific tool parameters
+before the tool is invoked or even passed to an LLM. These bound values are
+fixed and will not be requested or modified by the LLM during tool use.
+
+### Why Bind Parameters?
+
+- **Protecting sensitive information:**  API keys, secrets, etc.
+- **Enforcing consistency:** Ensuring specific values for certain parameters.
+- **Pre-filling known data:**  Providing defaults or context.
+
+{{< notice info >}}
+The parameter names used for binding (e.g., `"api_key"`) must exactly match the parameter names defined in the tool’s configuration within the Toolbox service.
+{{</notice>}}
+
+{{< notice >}}
+You do not need to modify the tool’s configuration in the Toolbox service to bind parameter values using the SDK.
+{{</notice>}}
+
+### Option A: Binding Parameters to a Loaded Tool
+
+Bind values to a tool object *after* it has been loaded. This modifies the
+specific tool instance.
+
+```py
+async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
+    tool = await toolbox.load_tool("my-tool")
+
+    bound_tool = tool.bind_param("param", "value")
+
+    # OR
+
+    bound_tool = tool.bind_params({"param": "value"})
+```
+
+### Option B: Binding Parameters While Loading Tools
+
+Specify bound parameters directly when loading tools. This applies the binding
+only to the tools loaded in that specific call.
+
+```py
+bound_tool = await toolbox.load_tool("my-tool", bound_params={"param": "value"})
+
+# OR
+
+bound_tools = await toolbox.load_toolset(bound_params={"param": "value"})
+```
+
+### Binding Dynamic Values
+
+Instead of a static value, you can bind a parameter to a synchronous or
+asynchronous function. This function will be called *each time* the tool is
+invoked to dynamically determine the parameter's value at runtime.
+
+{{< notice >}}
+ You don’t need to modify tool configurations to bind parameter values.
+{{</notice>}}
+
+```py
+async def get_dynamic_value():
+    # Logic to determine the value
+    return "dynamic_value"
+
+# Assuming `tool` is a loaded tool instance from a ToolboxClient
+dynamic_bound_tool = tool.bind_param("param", get_dynamic_value)
+```