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Harsh Jha
baca9735d6 chore: fixing metadata for adk doc 2026-02-03 12:32:34 +05:30
Harsh Jha
be4fc9e207 feat: python toolbox-adk sdk doc migration 2026-02-03 12:02:17 +05:30
Harsh Jha
f0c7eb129b Merge branch 'sdk-docs-migrate' of https://github.com/googleapis/genai-toolbox into py-sdk-docs 2026-01-12 13:23:55 +05:30
Harsh Jha
fef07c71a1 chore: fixed github link for python sdk 2026-01-12 13:23:12 +05:30
Harsh Jha
12b25a0beb chore: resolve pr comment 2025-12-12 12:35:17 +05:30
Harsh Jha
073c8b3268 feat: added python sdk introduction 2025-12-12 12:26:02 +05:30
4 changed files with 312 additions and 1085 deletions

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@@ -7,41 +7,6 @@ description: >
---
## Overview
![MCP Toolbox
Logo](https://raw.githubusercontent.com/googleapis/genai-toolbox/main/logo.png)
# MCP Toolbox SDKs for Go
[![License: Apache
2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Docs](https://img.shields.io/badge/Docs-MCP_Toolbox-blue)](https://googleapis.github.io/genai-toolbox/)
[![Discord](https://img.shields.io/badge/Discord-%235865F2.svg?style=flat&logo=discord&logoColor=white)](https://discord.gg/Dmm69peqjh)
[![Medium](https://img.shields.io/badge/Medium-12100E?style=flat&logo=medium&logoColor=white)](https://medium.com/@mcp_toolbox)
[![Go Report Card](https://goreportcard.com/badge/github.com/googleapis/mcp-toolbox-sdk-go)](https://goreportcard.com/report/github.com/googleapis/mcp-toolbox-sdk-go)
[![Module Version](https://img.shields.io/github/v/release/googleapis/mcp-toolbox-sdk-go)](https://img.shields.io/github/v/release/googleapis/mcp-toolbox-sdk-go)
[![Go Version](https://img.shields.io/github/go-mod/go-version/googleapis/mcp-toolbox-sdk-go)](https://img.shields.io/github/go-mod/go-version/googleapis/mcp-toolbox-sdk-go)
This repository contains the Go SDK designed to seamlessly integrate the
functionalities of the [MCP
Toolbox](https://github.com/googleapis/genai-toolbox) into your Gen AI
applications. The SDK allow you to load tools defined in Toolbox and use them
as standard Go tools within popular orchestration frameworks
or your custom code.
This simplifies the process of incorporating external functionalities (like
Databases or APIs) managed by Toolbox into your GenAI applications.
<!-- TOC -->
- [Overview](#overview)
- [Which Package Should I Use?](#which-package-should-i-use)
- [Available Packages](#available-packages)
- [Getting Started](#getting-started)
<!-- /TOC -->
## Overview
The MCP Toolbox service provides a centralized way to manage and expose tools
@@ -57,58 +22,4 @@ The Go SDK act as clients for that service. They handle the communication needed
By using the SDK, you can easily leverage your Toolbox-managed tools directly
within your Go applications or AI orchestration frameworks.
## Which Package Should I Use?
Choosing the right package depends on how you are building your application:
- [**`core`**](core/):
This is a framework-agnostic way to connect tools to popular frameworks
like Google GenAI, LangChain, etc.
- [**`tbadk`**](tbadk/):
This package provides a way to connect tools to ADK Go.
- [**`tbgenkit`**](tbgenkit/):
This package provides functionality to convert the Tool fetched using the core package
into a Genkit Go compatible tool.
## Available Packages
This repository hosts the following Go packages. See the package-specific
README for detailed installation and usage instructions:
| Package | Target Use Case | Integration | Path | Details (README) |
| :------ | :----------| :---------- | :---------------------- | :---------- |
| [`core`](core/) | Framework-agnostic / Custom applications | Use directly / Custom | `core/` | 📄 [View README](https://github.com/googleapis/mcp-toolbox-sdk-go/blob/main/core/README.md) |
| [`tbadk`](tbadk/) | ADK Go | Use directly | `tbadk/` | 📄 [View README](https://github.com/googleapis/mcp-toolbox-sdk-go/blob/main/tbadk/README.md) |
| [`tbgenkit`](tbgenkit/) | Genkit Go | Along with core | `tbgenkit/` | 📄 [View README](https://github.com/googleapis/mcp-toolbox-sdk-go/blob/main/tbgenkit/README.md) |
## Getting Started
To get started using Toolbox tools with an application, follow these general steps:
1. **Set up and Run the Toolbox Service:**
Before using the SDKs, you need the MCP Toolbox server running. Follow
the instructions here: [**Toolbox Getting Started
Guide**](https://github.com/googleapis/genai-toolbox?tab=readme-ov-file#getting-started)
2. **Install the Appropriate SDK:**
Choose the package based on your needs (see "[Which Package Should I Use?](#which-package-should-i-use)" above)
Use this command to install the SDK module
```bash
# For the core, framework-agnostic SDK
go get github.com/googleapis/mcp-toolbox-sdk-go
```
3. **Use the SDK:**
Consult the README for your chosen package (linked in the "[Available
Packages](#available-packages)" section above) for detailed instructions on
how to connect the client, load tool definitions, invoke tools, configure
authentication/binding, and integrate them into your application or
framework.
[Github](https://github.com/googleapis/mcp-toolbox-sdk-go)

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@@ -1,995 +0,0 @@
---
title: "Core Package"
linkTitle: "Core"
type: docs
weight: 1
---
![MCP Toolbox Logo](https://raw.githubusercontent.com/googleapis/genai-toolbox/main/logo.png)
# MCP Toolbox Core SDK
[![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
This SDK allows you to seamlessly integrate the functionalities of
[Toolbox](https://github.com/googleapis/genai-toolbox) allowing you to load and
use tools defined in the service as standard Go structs within your GenAI
applications.
This simplifies integrating external functionalities (like APIs, databases, or
custom logic) managed by the Toolbox into your workflows, especially those
involving Large Language Models (LLMs).
<!-- TOC ignore:true -->
<!-- TOC -->
- [MCP Toolbox Core SDK](#mcp-toolbox-core-sdk)
- [Installation](#installation)
- [Quickstart](#quickstart)
- [Usage](#usage)
- [Transport Protocols](#transport-protocols)
- [Supported Protocols](#supported-protocols)
- [Example](#example)
- [Loading Tools](#loading-tools)
- [Load a toolset](#load-a-toolset)
- [Load a single tool](#load-a-single-tool)
- [Invoking Tools](#invoking-tools)
- [Client to Server Authentication](#client-to-server-authentication)
- [When is Client-to-Server Authentication Needed?](#when-is-client-to-server-authentication-needed)
- [How it works](#how-it-works)
- [Configuration](#configuration)
- [Authenticating with Google Cloud Servers](#authenticating-with-google-cloud-servers)
- [Step by Step Guide for Cloud Run](#step-by-step-guide-for-cloud-run)
- [Authenticating Tools](#authenticating-tools)
- [When is Authentication Needed?](#when-is-authentication-needed)
- [Supported Authentication Mechanisms](#supported-authentication-mechanisms)
- [Step 1: Configure Tools in Toolbox Service](#step-1-configure-tools-in-toolbox-service)
- [Step 2: Configure SDK Client](#step-2-configure-sdk-client)
- [Provide an ID Token Retriever Function](#provide-an-id-token-retriever-function)
- [Option A: Add Default Authentication to a Client](#option-a-add-default-authentication-to-a-client)
- [Option B: Add Authentication to a Loaded Tool](#option-b-add-authentication-to-a-loaded-tool)
- [Option C: Add Authentication While Loading Tools](#option-c-add-authentication-while-loading-tools)
- [Complete Authentication Example](#complete-authentication-example)
- [Binding Parameter Values](#binding-parameter-values)
- [Why Bind Parameters?](#why-bind-parameters)
- [Option A: Add Default Bound Parameters to a Client](#option-a-add-default-bound-parameters-to-a-client)
- [Option B: Binding Parameters to a Loaded Tool](#option-b-binding-parameters-to-a-loaded-tool)
- [Option C: Binding Parameters While Loading Tools](#option-c-binding-parameters-while-loading-tools)
- [Binding Dynamic Values](#binding-dynamic-values)
- [Using with Orchestration Frameworks](#using-with-orchestration-frameworks)
- [Contributing](#contributing)
- [License](#license)
- [Support](#support)
<!-- /TOC -->
## Installation
```bash
go get github.com/googleapis/mcp-toolbox-sdk-go
```
This SDK is supported on Go version 1.24.4 and higher.
{{< notice note >}}
While the SDK itself is synchronous, you can execute its functions within goroutines to achieve asynchronous behavior.
{{< /notice >}}
## Quickstart
Here's a minimal example to get you started. Ensure your Toolbox service is
running and accessible.
```go
package main
import (
"context"
"fmt"
"github.com/googleapis/mcp-toolbox-sdk-go/core"
)
func quickstart() string {
ctx := context.Background()
inputs := map[string]any{"location": "London"}
client, err := core.NewToolboxClient("http://localhost:5000")
if err != nil {
return fmt.Sprintln("Could not start Toolbox Client", err)
}
tool, err := client.LoadTool("get_weather", ctx)
if err != nil {
return fmt.Sprintln("Could not load Toolbox Tool", err)
}
result, err := tool.Invoke(ctx, inputs)
if err != nil {
return fmt.Sprintln("Could not invoke tool", err)
}
return fmt.Sprintln(result)
}
func main() {
fmt.Println(quickstart())
}
```
## Usage
Import and initialize a Toolbox client, pointing it to the URL of your running
Toolbox service.
```go
import "github.com/googleapis/mcp-toolbox-sdk-go/core"
client, err := core.NewToolboxClient("http://localhost:5000")
```
All interactions for loading and invoking tools happen through this client.
{{< notice note >}}
For advanced use cases, you can provide an external custom `http.Client` during initialization (e.g., `core.NewToolboxClient(URL, core.WithHTTPClient(myClient)`).
If you provide your own session, you are responsible for managing its lifecycle; `ToolboxClient` *will not* close it.
{{< /notice >}}
{{< notice info >}}
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 `core.WithProtocol` 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 |
| :--- | :--- |
| `core.MCP` | **(Default)** Alias for the latest supported MCP version (currently `v2025-06-18`). |
| `core.Toolbox` | The native Toolbox HTTP protocol. |
| `core.MCPv20250618` | MCP Protocol version 2025-06-18. |
| `core.MCPv20250326` | MCP Protocol version 2025-03-26. |
| `core.MCPv20241105` | MCP Protocol version 2024-11-05. |
### Example
If you wish to use the native Toolbox protocol:
```go
import "github.com/googleapis/mcp-toolbox-sdk-go/core"
client, err := core.NewToolboxClient(
"http://localhost:5000",
core.WithProtocol(core.Toolbox),
)
```
If you want to pin the MCP Version 2025-03-26:
```go
import "github.com/googleapis/mcp-toolbox-sdk-go/core"
client, err := core.NewToolboxClient(
"http://localhost:5000",
core.WithProtocol(core.MCPv20250326),
)
```
## 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:
```go
// Load default toolset by providing an empty string as the name
tools, err := client.LoadToolset("", ctx)
// Load a specific toolset
tools, err := client.LoadToolset("my-toolset", ctx)
```
### Load a single tool
Loads a specific tool by its unique name. This provides fine-grained control.
```go
tool, err = client.LoadTool("my-tool", ctx)
```
## Invoking Tools
Once loaded, tools behave like Go structs. You invoke them using `Invoke` method
by passing arguments corresponding to the parameters defined in the tool's
configuration within the Toolbox service.
```go
tool, err = client.LoadTool("my-tool", ctx)
inputs := map[string]any{"location": "London"}
result, err := tool.Invoke(ctx, inputs)
```
{{< 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](https://googleapis.github.io/genai-toolbox/getting-started/local_quickstart).
{{< /notice >}}
## 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 `LoadTool`) will likely fail with `Unauthorized` errors.
### How it works
The `ToolboxClient` allows you to specify TokenSources 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:
```go
import "github.com/googleapis/mcp-toolbox-sdk-go/core"
tokenProvider := func() string {
return "header3_value"
}
staticTokenSource := oauth2.StaticTokenSource(&oauth2.Token{AccessToken: "header2_value"})
dynamicTokenSource := core.NewCustomTokenSource(tokenProvider)
client, err := core.NewToolboxClient(
"toolbox-url",
core.WithClientHeaderString("header1", "header1_value"),
core.WithClientHeaderTokenSource("header2", staticTokenSource),
core.WithClientHeaderTokenSource("header3", dynamicTokenSource),
)
```
### 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-go/blob/main/core/auth.go) 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**
```go
import "github.com/googleapis/mcp-toolbox-sdk-go/core"
import "context"
ctx := context.Background()
token, err := core.GetGoogleIDToken(ctx, URL)
client, err := core.NewToolboxClient(
URL,
core.WithClientHeaderString("Authorization", token),
)
// Now, you can use the client as usual.
```
## Authenticating Tools
{{< notice warning >}}
**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 requireauthentication 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 [Toolbox Service Documentation - Authenticated Parameters](https://googleapis.github.io/genai-toolbox/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 [Toolbox Service Documentation -
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 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 >}}
```go
func getAuthToken() string {
// ... 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 Default Authentication to a Client
You can add default tool level authentication to a client.
Every tool / toolset loaded by the client will contain the auth token.
```go
ctx := context.Background()
client, err := core.NewToolboxClient("http://127.0.0.1:5000",
core.WithDefaultToolOptions(
core.WithAuthTokenString("my-auth-1", "auth-value"),
),
)
AuthTool, err := client.LoadTool("my-tool", ctx)
```
#### Option B: 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.
```go
ctx := context.Background()
client, err := core.NewToolboxClient("http://127.0.0.1:5000")
tool, err := client.LoadTool("my-tool", ctx)
AuthTool, err := tool.ToolFrom(
core.WithAuthTokenSource("my-auth", headerTokenSource),
core.WithAuthTokenString("my-auth-1", "value"),
)
```
#### Option C: Add Authentication While Loading Tools
You can provide the token retriever(s) directly during the `LoadTool` or
`LoadToolset` 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.
```go
AuthTool, err := client.LoadTool("my-tool", ctx, core.WithAuthTokenString("my-auth-1", "value"))
// or
AuthTools, err := client.LoadToolset(
"my-toolset",
ctx,
core.WithAuthTokenString("my-auth-1", "value"),
)
```
{{< notice note >}}
Adding auth tokens during loading only affect the tools loaded within that call.
{{< /notice >}}
### Complete Authentication Example
```go
import "github.com/googleapis/mcp-toolbox-sdk-go/core"
import "fmt"
func getAuthToken() string {
// ... 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
}
func main() {
ctx := context.Background()
inputs := map[string]any{"input": "some input"}
dynamicTokenSource := core.NewCustomTokenSource(getAuthToken)
client, err := core.NewToolboxClient("http://127.0.0.1:5000")
tool, err := client.LoadTool("my-tool", ctx)
AuthTool, err := tool.ToolFrom(core.WithAuthTokenSource("my_auth", dynamicTokenSource))
result, err := AuthTool.Invoke(ctx, inputs)
fmt.Println(result)
}
```
{{< notice note >}}
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 >}}
## Binding Parameter Values
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 note >}}
You do not need to modify the tool's configuration in the Toolbox service to bind parameter values using the SDK.
{{< /notice >}}
#### Option A: Add Default Bound Parameters to a Client
You can add default tool level bound parameters to a client. Every tool / toolset
loaded by the client will have the bound parameter.
```go
ctx := context.Background()
client, err := core.NewToolboxClient("http://127.0.0.1:5000",
core.WithDefaultToolOptions(
core.WithBindParamString("param1", "value"),
),
)
boundTool, err := client.LoadTool("my-tool", ctx)
```
### Option B: Binding Parameters to a Loaded Tool
Bind values to a tool object *after* it has been loaded. This modifies the
specific tool instance.
```go
client, err := core.NewToolboxClient("http://127.0.0.1:5000")
tool, err := client.LoadTool("my-tool", ctx)
boundTool, err := tool.ToolFrom(
core.WithBindParamString("param1", "value"),
core.WithBindParamString("param2", "value")
)
```
### Option C: 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.
```go
boundTool, err := client.LoadTool("my-tool", ctx, core.WithBindParamString("param", "value"))
// OR
boundTool, err := client.LoadToolset("", ctx, core.WithBindParamString("param", "value"))
```
{{< notice note >}} Bound values during loading only affect the tools loaded in that call. {{< /notice >}}
### 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.
Functions with the return type (data_type, error) can be provided.
```go
getDynamicValue := func() (string, error) { return "req-123", nil }
dynamicBoundTool, err := tool.ToolFrom(core.WithBindParamStringFunc("param", getDynamicValue))
```
{{< notice info >}} You don't need to modify tool configurations to bind parameter values. {{< /notice >}}
# Using with Orchestration Frameworks
To see how the MCP Toolbox Go SDK works with orchestration frameworks, check out the end-to-end examples in the [/samples/](https://github.com/googleapis/mcp-toolbox-sdk-go/tree/main/core/samples) folder.
Use the [tbgenkit package](https://github.com/googleapis/mcp-toolbox-sdk-go/tree/main/tbgenkit) to convert Toolbox Tools into Genkit compatible tools.
# Contributing
Contributions are welcome! Please refer to the [DEVELOPER.md](https://github.com/googleapis/mcp-toolbox-sdk-go/blob/main/DEVELOPER.md)
file for guidelines on how to set up a development environment and run tests.
# License
This project is licensed under the Apache License 2.0. See the
[LICENSE](https://github.com/googleapis/mcp-toolbox-sdk-go/blob/main/LICENSE) file for details.
# Support
If you encounter issues or have questions, check the existing [GitHub Issues](https://github.com/googleapis/genai-toolbox/issues) for the main Toolbox project.
# Samples for Reference
These samples demonstrate how to integrate the MCP Toolbox Go Core SDK with popular orchestration frameworks.
<details>
<summary>Google GenAI</summary>
```go
// This sample demonstrates integration with the standard Google GenAI framework.
package main
import (
"context"
"encoding/json"
"fmt"
"log"
"os"
"github.com/googleapis/mcp-toolbox-sdk-go/core"
"google.golang.org/genai"
)
// ConvertToGenaiTool translates a ToolboxTool into the genai.FunctionDeclaration format.
func ConvertToGenaiTool(toolboxTool *core.ToolboxTool) *genai.Tool {
inputschema, err := toolboxTool.InputSchema()
if err != nil {
return &genai.Tool{}
}
var schema *genai.Schema
_ = json.Unmarshal(inputschema, &schema)
// First, create the function declaration.
funcDeclaration := &genai.FunctionDeclaration{
Name: toolboxTool.Name(),
Description: toolboxTool.Description(),
Parameters: schema,
}
// Then, wrap the function declaration in a genai.Tool struct.
return &genai.Tool{
FunctionDeclarations: []*genai.FunctionDeclaration{funcDeclaration},
}
}
// printResponse extracts and prints the relevant parts of the model's response.
func printResponse(resp *genai.GenerateContentResponse) {
for _, cand := range resp.Candidates {
if cand.Content != nil {
for _, part := range cand.Content.Parts {
fmt.Println(part.Text)
}
}
}
}
func main() {
// Setup
ctx := context.Background()
apiKey := os.Getenv("GOOGLE_API_KEY")
toolboxURL := "http://localhost:5000"
// Initialize the Google GenAI client using the explicit ClientConfig.
client, err := genai.NewClient(ctx, &genai.ClientConfig{
APIKey: apiKey,
})
if err != nil {
log.Fatalf("Failed to create Google GenAI client: %v", err)
}
// Initialize the MCP Toolbox client.
toolboxClient, err := core.NewToolboxClient(toolboxURL)
if err != nil {
log.Fatalf("Failed to create Toolbox client: %v", err)
}
// Load the tools using the MCP Toolbox SDK.
tools, err := toolboxClient.LoadToolset("my-toolset", ctx)
if err != nil {
log.Fatalf("Failed to load tools: %v\nMake sure your Toolbox server is running and the tool is configured.", err)
}
genAITools := make([]*genai.Tool, len(tools))
toolsMap := make(map[string]*core.ToolboxTool, len(tools))
for i, tool := range tools {
// Convert the tools into usable format
genAITools[i] = ConvertToGenaiTool(tool)
// Add tool to a map for lookup later
toolsMap[tool.Name()] = tool
}
// Set up the generative model with the available tool.
modelName := "gemini-2.0-flash"
query := "Find hotels in Basel with Basel in it's name and share the names with me"
// Create the initial content prompt for the model.
contents := []*genai.Content{
genai.NewContentFromText(query, genai.RoleUser),
}
config := &genai.GenerateContentConfig{
Tools: genAITools,
ToolConfig: &genai.ToolConfig{
FunctionCallingConfig: &genai.FunctionCallingConfig{
Mode: genai.FunctionCallingConfigModeAny,
},
},
}
genContentResp, _ := client.Models.GenerateContent(ctx, modelName, contents, config)
printResponse(genContentResp)
functionCalls := genContentResp.FunctionCalls()
if len(functionCalls) == 0 {
log.Println("No function call returned by the AI. The model likely answered directly.")
return
}
// Process the first function call (the example assumes one for simplicity).
fc := functionCalls[0]
log.Printf("--- Gemini requested function call: %s ---\n", fc.Name)
log.Printf("--- Arguments: %+v ---\n", fc.Args)
var toolResultString string
if fc.Name == "search-hotels-by-name" {
tool := toolsMap["search-hotels-by-name"]
toolResult, err := tool.Invoke(ctx, fc.Args)
toolResultString = fmt.Sprintf("%v", toolResult)
if err != nil {
log.Fatalf("Failed to execute tool '%s': %v", fc.Name, err)
}
} else {
log.Println("LLM did not request our tool")
}
resultContents := []*genai.Content{
genai.NewContentFromText("The tool returned this result, share it with the user based of their previous querys"+toolResultString, genai.RoleUser),
}
finalResponse, err := client.Models.GenerateContent(ctx, modelName, resultContents, &genai.GenerateContentConfig{})
if err != nil {
log.Fatalf("Error calling GenerateContent (with function result): %v", err)
}
log.Println("=== Final Response from Model (after processing function result) ===")
printResponse(finalResponse)
}
```
</details>
<details>
<summary>LangChain</summary>
```go
// This sample demonstrates how to use Toolbox tools as function definitions in LangChain Go.
package main
import (
"context"
"encoding/json"
"fmt"
"log"
"os"
"github.com/googleapis/mcp-toolbox-sdk-go/core"
"github.com/tmc/langchaingo/llms"
"github.com/tmc/langchaingo/llms/googleai"
)
// ConvertToLangchainTool converts a generic core.ToolboxTool into a LangChainGo llms.Tool.
func ConvertToLangchainTool(toolboxTool *core.ToolboxTool) llms.Tool {
// Fetch the tool's input schema
inputschema, err := toolboxTool.InputSchema()
if err != nil {
return llms.Tool{}
}
var paramsSchema map[string]any
_ = json.Unmarshal(inputschema, &paramsSchema)
// Convert into LangChain's llms.Tool
return llms.Tool{
Type: "function",
Function: &llms.FunctionDefinition{
Name: toolboxTool.Name(),
Description: toolboxTool.Description(),
Parameters: paramsSchema,
},
}
}
func main() {
genaiKey := os.Getenv("GOOGLE_API_KEY")
toolboxURL := "http://localhost:5000"
ctx := context.Background()
// Initialize the Google AI client (LLM).
llm, err := googleai.New(ctx, googleai.WithAPIKey(genaiKey), googleai.WithDefaultModel("gemini-1.5-flash"))
if err != nil {
log.Fatalf("Failed to create Google AI client: %v", err)
}
// Initialize the MCP Toolbox client.
toolboxClient, err := core.NewToolboxClient(toolboxURL)
if err != nil {
log.Fatalf("Failed to create Toolbox client: %v", err)
}
// Load the tools using the MCP Toolbox SDK.
tools, err := toolboxClient.LoadToolset("my-toolset", ctx)
if err != nil {
log.Fatalf("Failed to load tools: %v\nMake sure your Toolbox server is running and the tool is configured.", err)
}
toolsMap := make(map[string]*core.ToolboxTool, len(tools))
langchainTools := make([]llms.Tool, len(tools))
for i, tool := range tools {
// Convert the loaded ToolboxTools into the format LangChainGo requires.
langchainTools[i] = ConvertToLangchainTool(tool)
// Add tool to a map for lookup later
toolsMap[tool.Name()] = tool
}
// Start the conversation history.
messageHistory := []llms.MessageContent{
llms.TextParts(llms.ChatMessageTypeHuman, "Find hotels in Basel with Basel in it's name."),
}
// Make the first call to the LLM, making it aware of the tool.
resp, err := llm.GenerateContent(ctx, messageHistory, llms.WithTools(langchainTools))
if err != nil {
log.Fatalf("LLM call failed: %v", err)
}
// Add the model's response (which should be a tool call) to the history.
respChoice := resp.Choices[0]
assistantResponse := llms.TextParts(llms.ChatMessageTypeAI, respChoice.Content)
for _, tc := range respChoice.ToolCalls {
assistantResponse.Parts = append(assistantResponse.Parts, tc)
}
messageHistory = append(messageHistory, assistantResponse)
// Process each tool call requested by the model.
for _, tc := range respChoice.ToolCalls {
toolName := tc.FunctionCall.Name
switch tc.FunctionCall.Name {
case "search-hotels-by-name":
var args map[string]any
if err := json.Unmarshal([]byte(tc.FunctionCall.Arguments), &args); err != nil {
log.Fatalf("Failed to unmarshal arguments for tool '%s': %v", toolName, err)
}
tool := toolsMap["search-hotels-by-name"]
toolResult, err := tool.Invoke(ctx, args)
if err != nil {
log.Fatalf("Failed to execute tool '%s': %v", toolName, err)
}
// Create the tool call response message and add it to the history.
toolResponse := llms.MessageContent{
Role: llms.ChatMessageTypeTool,
Parts: []llms.ContentPart{
llms.ToolCallResponse{
Name: toolName,
Content: fmt.Sprintf("%v", toolResult),
},
},
}
messageHistory = append(messageHistory, toolResponse)
default:
log.Fatalf("got unexpected function call: %v", tc.FunctionCall.Name)
}
}
// Final LLM Call for Natural Language Response
log.Println("Sending tool response back to LLM for a final answer...")
// Call the LLM again with the updated history, which now includes the tool's result.
finalResp, err := llm.GenerateContent(ctx, messageHistory)
if err != nil {
log.Fatalf("Final LLM call failed: %v", err)
}
// Display the Result
fmt.Println("\n======================================")
fmt.Println("Final Response from LLM:")
fmt.Println(finalResp.Choices[0].Content)
fmt.Println("======================================")
}
```
</details>
<details>
<summary>OpenAI</summary>
```go
// This sample demonstrates integration with the OpenAI Go client.
package main
import (
"context"
"encoding/json"
"fmt"
"log"
"github.com/googleapis/mcp-toolbox-sdk-go/core"
openai "github.com/openai/openai-go"
)
// ConvertToOpenAITool converts a ToolboxTool into the go-openai library's Tool format.
func ConvertToOpenAITool(toolboxTool *core.ToolboxTool) openai.ChatCompletionToolParam {
// Get the input schema
jsonSchemaBytes, err := toolboxTool.InputSchema()
if err != nil {
return openai.ChatCompletionToolParam{}
}
// Unmarshal the JSON bytes into FunctionParameters
var paramsSchema openai.FunctionParameters
if err := json.Unmarshal(jsonSchemaBytes, &paramsSchema); err != nil {
return openai.ChatCompletionToolParam{}
}
// Create and return the final tool parameter struct.
return openai.ChatCompletionToolParam{
Function: openai.FunctionDefinitionParam{
Name: toolboxTool.Name(),
Description: openai.String(toolboxTool.Description()),
Parameters: paramsSchema,
},
}
}
func main() {
// Setup
ctx := context.Background()
toolboxURL := "http://localhost:5000"
openAIClient := openai.NewClient()
// Initialize the MCP Toolbox client.
toolboxClient, err := core.NewToolboxClient(toolboxURL)
if err != nil {
log.Fatalf("Failed to create Toolbox client: %v", err)
}
// Load the tools using the MCP Toolbox SDK.
tools, err := toolboxClient.LoadToolset("my-toolset", ctx)
if err != nil {
log.Fatalf("Failed to load tool : %v\nMake sure your Toolbox server is running and the tool is configured.", err)
}
openAITools := make([]openai.ChatCompletionToolParam, len(tools))
toolsMap := make(map[string]*core.ToolboxTool, len(tools))
for i, tool := range tools {
// Convert the Toolbox tool into the openAI FunctionDeclaration format.
openAITools[i] = ConvertToOpenAITool(tool)
// Add tool to a map for lookup later
toolsMap[tool.Name()] = tool
}
question := "Find hotels in Basel with Basel in it's name "
params := openai.ChatCompletionNewParams{
Messages: []openai.ChatCompletionMessageParamUnion{
openai.UserMessage(question),
},
Tools: openAITools,
Seed: openai.Int(0),
Model: openai.ChatModelGPT4o,
}
// Make initial chat completion request
completion, err := openAIClient.Chat.Completions.New(ctx, params)
if err != nil {
panic(err)
}
toolCalls := completion.Choices[0].Message.ToolCalls
// Return early if there are no tool calls
if len(toolCalls) == 0 {
fmt.Printf("No function call")
return
}
// If there was a function call, continue the conversation
params.Messages = append(params.Messages, completion.Choices[0].Message.ToParam())
for _, toolCall := range toolCalls {
if toolCall.Function.Name == "search-hotels-by-name" {
// Extract the location from the function call arguments
var args map[string]interface{}
tool := toolsMap["search-hotels-by-name"]
err := json.Unmarshal([]byte(toolCall.Function.Arguments), &args)
if err != nil {
panic(err)
}
result, err := tool.Invoke(ctx, args)
if err != nil {
log.Fatal("Could not invoke tool", err)
}
params.Messages = append(params.Messages, openai.ToolMessage(result.(string), toolCall.ID))
}
}
completion, err = openAIClient.Chat.Completions.New(ctx, params)
if err != nil {
panic(err)
}
fmt.Println(completion.Choices[0].Message.Content)
}
```
</details>

View File

@@ -22,4 +22,36 @@ These Python SDKs act as clients for that service. They handle the communication
By using these SDKs, you can easily leverage your Toolbox-managed tools directly
within your Python applications or AI orchestration frameworks.
[Github](https://github.com/googleapis/mcp-toolbox-sdk-python)
## Which Package Should I Use?
Choosing the right package depends on how you are building your application:
* [`toolbox-langchain`](langchain):
Use this package if you are building your application using the LangChain or
LangGraph frameworks. It provides tools that are directly compatible with the
LangChain ecosystem (`BaseTool` interface), simplifying integration.
* [`toolbox-llamaindex`](llamaindex):
Use this package if you are building your application using the LlamaIndex framework.
It provides tools that are directly compatible with the
LlamaIndex ecosystem (`BaseTool` interface), simplifying integration.
* [`toolbox-core`](core):
Use this package if you are not using LangChain/LangGraph or any other
orchestration framework, or if you need a framework-agnostic way to interact
with Toolbox tools (e.g., for custom orchestration logic or direct use in
Python scripts).
## Available Packages
This repository hosts the following Python packages. See the package-specific
README for detailed installation and usage instructions:
| Package | Target Use Case | Integration | Path | Details (README) | PyPI Status |
| :------ | :---------- | :---------- | :---------------------- | :---------- | :---------
| `toolbox-core` | Framework-agnostic / Custom applications | Use directly / Custom | `packages/toolbox-core/` | 📄 [View README](https://github.com/googleapis/mcp-toolbox-sdk-python/blob/main/packages/toolbox-core/README.md) | ![pypi version](https://img.shields.io/pypi/v/toolbox-core.svg) |
| `toolbox-langchain` | LangChain / LangGraph applications | LangChain / LangGraph | `packages/toolbox-langchain/` | 📄 [View README](https://github.com/googleapis/mcp-toolbox-sdk-python/blob/main/packages/toolbox-langchain/README.md) | ![pypi version](https://img.shields.io/pypi/v/toolbox-langchain.svg) |
| `toolbox-llamaindex` | LlamaIndex applications | LlamaIndex | `packages/toolbox-llamaindex/` | 📄 [View README](https://github.com/googleapis/mcp-toolbox-sdk-python/blob/main/packages/toolbox-llamaindex/README.md) | ![pypi version](https://img.shields.io/pypi/v/toolbox-llamaindex.svg) |
{{< notice note >}}
Source code for [python-sdk](https://github.com/googleapis/mcp-toolbox-sdk-python)
{{< /notice >}}

View File

@@ -0,0 +1,279 @@
---
title: "Adk"
type: docs
weight: 8
description: >
MCP Toolbox ADK SDK for integrating functionalities of MCP Toolbox into your apps.
---
## Overview
The `toolbox-adk` 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-adk
```
## Usage
The primary entry point is the `ToolboxToolset`, which loads tools from a remote Toolbox server and adapts them for use with ADK agents.
{{< notice note>}}
The `ToolboxToolset` in this package mirrors the `ToolboxToolset` in the [`adk-python`](https://github.com/google/adk-python) package. The `adk-python` version is a shim that delegates all functionality to this implementation.
{{< /notice >}}
```python
from toolbox_adk import ToolboxToolset
from google.adk.agents import Agent
# Create the Toolset
toolset = ToolboxToolset(
server_url="http://127.0.0.1:5000"
)
# Use in your ADK Agent
agent = Agent(tools=[toolset])
```
## 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 toolset 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` | The native Toolbox HTTP protocol. |
| `Protocol.MCP_v20251125` | MCP Protocol version 2025-11-25. |
| `Protocol.MCP_v20250618` | MCP Protocol version 2025-06-18. |
| `Protocol.MCP_v20250326` | MCP Protocol version 2025-03-26. |
| `Protocol.MCP_v20241105` | MCP Protocol version 2024-11-05. |
### Example
If you wish to use the native Toolbox protocol:
```python
from toolbox_adk import ToolboxToolset
from toolbox_core.protocol import Protocol
toolset = ToolboxToolset(
server_url="http://127.0.0.1:5000",
protocol=Protocol.TOOLBOX
)
```
If you want to pin the MCP Version 2025-03-26:
```python
from toolbox_adk import ToolboxToolset
from toolbox_core.protocol import Protocol
toolset = ToolboxToolset(
server_url="http://127.0.0.1:5000",
protocol=Protocol.MCP_v20250326
)
```
{{< notice tip>}}
By default, it uses **Toolbox Identity** (no authentication), which is suitable for local development.
For production environments (Cloud Run, GKE) or accessing protected resources, see the [Authentication](#authentication) section for strategies like Workload Identity or OAuth2.
{{< /notice >}}
## Authentication
The `ToolboxToolset` requires credentials to authenticate with the Toolbox server. You can configure these credentials using the `CredentialStrategy` factory methods.
The strategies handle two main types of authentication:
* **Client-to-Server**: Securing the connection to the Toolbox server (e.g., Workload Identity, API keys).
* **User Identity**: Authenticating the end-user for specific tools (e.g., 3-legged OAuth).
### 1. Workload Identity (ADC)
*Recommended for Cloud Run, GKE, or local development with `gcloud auth login`.*
Uses the agent's Application Default Credentials (ADC) to generate an OIDC token. This is the standard way for one service to authenticate to another on Google Cloud.
```python
from toolbox_adk import CredentialStrategy, ToolboxToolset
# target_audience: The URL of your Toolbox server
creds = CredentialStrategy.workload_identity(target_audience="https://my-toolbox-service.run.app")
toolset = ToolboxToolset(
server_url="https://my-toolbox-service.run.app",
credentials=creds
)
```
### 2. User Identity (OAuth2)
*Recommended for tools that act on behalf of the user.*
Configures the ADK-native interactive 3-legged OAuth flow to get consent and credentials from the end-user at runtime. This strategy is passed to the `ToolboxToolset` just like any other credential strategy.
```python
from toolbox_adk import CredentialStrategy, ToolboxToolset
creds = CredentialStrategy.user_identity(
client_id="YOUR_CLIENT_ID",
client_secret="YOUR_CLIENT_SECRET",
scopes=["https://www.googleapis.com/auth/cloud-platform"]
)
# The toolset will now initiate OAuth flows when required by tools
toolset = ToolboxToolset(
server_url="...",
credentials=creds
)
```
### 3. API Key
*Use a static API key passed in a specific header (default: `X-API-Key`).*
```python
from toolbox_adk import CredentialStrategy
# Default header: X-API-Key
creds = CredentialStrategy.api_key(key="my-secret-key")
# Custom header
creds = CredentialStrategy.api_key(key="my-secret-key", header_name="X-My-Header")
```
### 4. HTTP Bearer Token
*Manually supply a static bearer token.*
```python
from toolbox_adk import CredentialStrategy
creds = CredentialStrategy.manual_token(token="your-static-bearer-token")
```
### 5. Manual Google Credentials
*Use an existing `google.auth.credentials.Credentials` object.*
```python
from toolbox_adk import CredentialStrategy
import google.auth
creds_obj, _ = google.auth.default()
creds = CredentialStrategy.manual_credentials(credentials=creds_obj)
```
### 6. Toolbox Identity (No Auth)
*Use this if your Toolbox server does not require authentication (e.g., local development).*
```python
from toolbox_adk import CredentialStrategy
creds = CredentialStrategy.toolbox_identity()
```
### 7. Native ADK Integration
*Convert ADK-native `AuthConfig` or `AuthCredential` objects.*
```python
from toolbox_adk import CredentialStrategy
# From AuthConfig
creds = CredentialStrategy.from_adk_auth_config(auth_config)
# From AuthCredential + AuthScheme
creds = CredentialStrategy.from_adk_credentials(auth_credential, scheme)
```
### 8. Tool-Specific Authentication
*Resolve authentication tokens dynamically for specific tools.*
Some tools may define their own authentication requirements (e.g., Salesforce OAuth, GitHub PAT) via `authSources` in their schema. You can provide a mapping of getters to resolve these tokens at runtime.
```python
async def get_salesforce_token():
# Fetch token from secret manager or reliable source
return "sf-access-token"
toolset = ToolboxToolset(
server_url="...",
auth_token_getters={
"salesforce-auth": get_salesforce_token, # Async callable
"github-pat": lambda: "my-pat-token" # Sync callable or static lambda
}
)
```
## Advanced Configuration
### Additional Headers
You can inject custom headers into every request made to the Toolbox server. This is useful for passing tracing IDs, API keys, or other metadata.
```python
toolset = ToolboxToolset(
server_url="...",
additional_headers={
"X-Trace-ID": "12345",
"X-My-Header": lambda: get_dynamic_header_value() # Can be a callable
}
)
```
### Global Parameter Binding
Bind values to tool parameters globally across all loaded tools. These values will be **fixed** and **hidden** from the LLM.
* **Schema Hiding**: The bound parameters are removed from the tool schema sent to the model, simplifying the context window.
* **Auto-Injection**: The values are automatically injected into the tool arguments during execution.
```python
toolset = ToolboxToolset(
server_url="...",
bound_params={
# 'region' will be removed from the LLM schema and injected automatically
"region": "us-central1",
"api_key": lambda: get_api_key() # Can be a callable
}
)
```
### Usage with Hooks
You can attach `pre_hook` and `post_hook` functions to execute logic before and after every tool invocation.
{{< notice note>}}
The `pre_hook` can modify `context.arguments` to dynamically alter the inputs passed to the tool.
{{< /notice >}}
```python
from google.adk.tools.tool_context import ToolContext
from typing import Any, Dict, Optional
async def log_start(context: ToolContext, args: Dict[str, Any]):
print(f"Starting tool with args: {args}")
# context is the ADK ToolContext
# Example: Inject or modify arguments
# args["user_id"] = "123"
async def log_end(context: ToolContext, args: Dict[str, Any], result: Optional[Any], error: Optional[Exception]):
print("Finished tool execution")
# Inspect result or error
if error:
print(f"Tool failed: {error}")
else:
print(f"Tool succeeded with result: {result}")
toolset = ToolboxToolset(
server_url="...",
pre_hook=log_start,
post_hook=log_end
)
```