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@toolbox-sdk/core - npm

MCP Toolbox SDKs for Javascript

This SDK allows you to seamlessly integrate the functionalities of Toolbox allowing you to load and use tools defined in the service as standard JS functions 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).

This SDK is a standard Node.js package built with TypeScript, ensuring broad compatibility with the modern JavaScript ecosystem.

Node.js: Actively supported on Node.js v18.x and higher. The package is compatible with both modern ES Modules (import) and legacy CommonJS (require).
TypeScript: The SDK is written in TypeScript and ships with its own type declarations, providing a first-class development experience with autocompletion and type-checking out of the box.
JavaScript: Fully supports modern JavaScript in Node.js environments.

npm install @toolbox-sdk/core

Here's a minimal example to get you started. Ensure your Toolbox service is running and accessible.

import { ToolboxClient } from '@toolbox-sdk/core';  
const client = new ToolboxClient(URL);  

async function quickstart() {  
  try {  
      const tools = await client.loadToolset();  
      // Use tools  
  } catch (error) {  
      console.error("unable to load toolset:", error.message);  
  }  
}  
quickstart();

[!NOTE] This guide uses modern ES Module (import) syntax. If your project uses CommonJS, you can import the library using require: const { ToolboxClient } = require('@toolbox-sdk/core');.

Import and initialize a Toolbox client, pointing it to the URL of your running Toolbox service.

import { ToolboxClient } from '@toolbox-sdk/core';

// Replace with the actual URL where your Toolbox service is running
const URL = 'http://127.0.0.1:5000';

let client = new ToolboxClient(URL);
const tools = await client.loadToolset();

// Use the client and tools as per requirement

All interactions for loading and invoking tools happen through this client.

[!IMPORTANT] 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.

[!NOTE] For advanced use cases, you can provide an external AxiosInstance during initialization (e.g., ToolboxClient(url, my_session)).

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.

A toolset is a collection of related tools. You can load all tools in a toolset or a specific one:

// Load all tools
const tools = await toolbox.loadToolset()

// Load a specific toolset
const tools = await toolbox.loadToolset("my-toolset")

Loads a specific tool by its unique name. This provides fine-grained control.

const tool = await toolbox.loadTool("my-tool")

Once loaded, tools behave like awaitable JS functions. You invoke them using await and pass arguments corresponding to the parameters defined in the tool's configuration within the Toolbox service.

const tool = await toolbox.loadTool("my-tool")
const result = await tool({a: 5, b: 2})

[!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.

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, 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.

The ToolboxClient allows you to specify functions 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.

You can configure these dynamic headers as seen below:

import { ToolboxClient } from '@toolbox-sdk/core';
import {getGoogleIdToken} from '@toolbox-sdk/core/auth'

const URL = 'http://127.0.0.1:5000';
const getGoogleIdTokenGetter = () => getGoogleIdToken(URL);
const client = new ToolboxClient(URL, null, {"Authorization": getGoogleIdTokenGetter});

// Use the client as usual

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 provides utility functions.

Step by Step Guide for Cloud Run

Configure Permissions: Grant 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.
Configure Credentials
- Local Development: Set up ADC.
- 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.

Connect to the Toolbox Server

import { ToolboxClient } from '@toolbox-sdk/core';
import {getGoogleIdToken} from '@toolbox-sdk/core/auth'

const URL = 'http://127.0.0.1:5000';
const getGoogleIdTokenGetter = () => getGoogleIdToken(URL);
const client = new ToolboxClient(URL, null, {"Authorization": getGoogleIdTokenGetter});

// Use the client as usual

[!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 require authentication tokens). Using plain HTTP lacks encryption and exposes your application and data to significant security risks, such as eavesdropping and tampering.

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

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 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).

[!IMPORTANT] 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.

async function getAuthToken() {
    // ... 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
}

[!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.

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.

const URL = 'http://127.0.0.1:5000';
let client = new ToolboxClient(URL);
let tool = await client.loadTool("my-tool")

const authTool = tool.addAuthTokenGetter("my_auth", get_auth_token)  // Single token

// OR

const multiAuthTool = tool.addAuthTokenGetters({
    "my_auth_1": getAuthToken1,
    "my_auth_2": getAuthToken2,
})  // Multiple tokens

Option B: 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.

const authTool = await toolbox.loadTool("toolName", {"myAuth": getAuthToken})

// OR

const authTools = await toolbox.loadToolset({"myAuth": getAuthToken})

[!NOTE] Adding auth tokens during loading only affect the tools loaded within that call.

Complete Authentication Example

import { ToolboxClient } from '@toolbox-sdk/core';

async function getAuthToken() {
    // ... 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
}

const URL = 'http://127.0.0.1:5000';
let client = new ToolboxClient(URL);
const tool = await client.loadTool("my-tool");
const authTool = tool.addAuthTokenGetters({"my_auth": getAuthToken});
const result = await authTool({input:"some input"});
console.log(result);

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.

Protecting sensitive information: API keys, secrets, etc.
Enforcing consistency: Ensuring specific values for certain parameters.
Pre-filling known data: Providing defaults or context.

[!IMPORTANT] 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.

[!NOTE] You do not need to modify the tool's configuration in the Toolbox service to bind parameter values using the SDK.

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.

import { ToolboxClient } from '@toolbox-sdk/core';

const URL = 'http://127.0.0.1:5000';
let client = new ToolboxClient(URL);
const tool = await client.loadTool("my-tool");

const boundTool = tool.bindParam("param", "value");

// OR

const boundTool = tool.bindParams({"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.

const boundTool = await client.loadTool("my-tool", null, {"param": "value"})

// OR

const boundTools = await client.loadToolset(null, {"param": "value"})

[!NOTE] Bound values during loading only affect the tools loaded in that call.

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.

async function getDynamicValue() {
    // Logic to determine the value
    return "dynamicValue";
}

const dynamicBoundTool = tool.bindParam("param", getDynamicValue)

[!IMPORTANT] You don't need to modify tool configurations to bind parameter values.

Using with Orchestration Frameworks Langchain

LangchainJS

import {ToolboxClient} from "@toolbox-sdk/core"
import { tool } from "@langchain/core/tools";

let client = ToolboxClient(URL)
multiplyTool = await client.loadTool("multiply")

const multiplyNumbers = tool(multiplyTool, {
    name: multiplyTool.getName(),
    description: multiplyTool.getDescription(),
    schema: multiplyTool.getParams()
});

await multiplyNumbers.invoke({ a: 2, b: 3 });

The multiplyNumbers tool is compatible with Langchain/Langraph agents such as React Agents.

LlamaIndex

LlamaindexTS

import {ToolboxClient} from "@toolbox-sdk/core"
import { tool } from "llamaindex";

let client = ToolboxClient(URL)
multiplyTool = await client.loadTool("multiply")

const multiplyNumbers = tool({
    name: multiplyTool.getName(),
    description: multiplyTool.getDescription(),
    parameters: multiplyTool.getParams(),
    execute: mutliplyTool
});

await multiplyNumbers.call({ a: 2, b: 3 });

The multiplyNumbers tool is compatible with LlamaIndex agents and agent workflows.

Genkit

GenkitJS

import {ToolboxClient} from "@toolbox-sdk/core"
import { genkit, z } from 'genkit';
import { googleAI } from '@genkit-ai/googleai';


let client = ToolboxClient(URL)
multiplyTool = await client.loadTool("multiply")

const ai = genkit({
  plugins: [googleAI()],
  model: googleAI.model('gemini-1.5-pro'),
});

const multiplyNumbers = ai.defineTool({
    name: multiplyTool.getName(),
    description: multiplyTool.getDescription(),
    inputSchema: multiplyTool.getParams(),
  },
  multiplyTool,
);

await ai.generate({
  prompt: 'Can you multiply 5 and 7?',
  tools: [multiplyNumbers],
});

Contributions are welcome! Please refer to the DEVELOPER.md file for guidelines on how to set up a development environment and run tests.

This project is licensed under the Apache License 2.0. See the LICENSE file for details.

If you encounter issues or have questions, check the existing GitHub Issues for the main Toolbox project.

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