MCP Toolbox for Databases

[!NOTE]
MCP Toolbox for Databases is currently in beta, and may see breaking
changes until the first stable release (v1.0).

MCP Toolbox for Databases is an open source MCP server for databases. It enables
you to develop tools easier, faster, and more securely by handling the complexities
such as connection pooling, authentication, and more.

This README provides a brief overview. For comprehensive details, see the full
documentation.

[!NOTE]
This solution was originally named “Gen AI Toolbox for Databases” as
its initial development predated MCP, but was renamed to align with recently
added MCP compatibility.

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Table of Contents

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• Why Toolbox?
• General Architecture
• Getting Started
• Installing the server
• Running the server
• Integrating your application
• Configuration
• Sources
• Tools
• Toolsets
• Versioning
• Contributing

<!-- /TOC -->

Why Toolbox?

Toolbox helps you build Gen AI tools that let your agents access data in your
database. Toolbox provides:

• Simplified development: Integrate tools to your agent in less than 10
  lines of code, reuse tools between multiple agents or frameworks, and deploy
  new versions of tools more easily.
• Better performance: Best practices such as connection pooling,
  authentication, and more.
• Enhanced security: Integrated auth for more secure access to your data
• End-to-end observability: Out of the box metrics and tracing with built-in
  support for OpenTelemetry.

⚡ Supercharge Your Workflow with an AI Database Assistant ⚡

Stop context-switching and let your AI assistant become a true co-developer. By
connecting your IDE to your databases with MCP Toolbox, you can
delegate complex and time-consuming database tasks, allowing you to build faster
and focus on what matters. This isn't just about code completion; it's about
giving your AI the context it needs to handle the entire development lifecycle.

Here’s how it will save you time:

• Query in Plain English: Interact with your data using natural language
  right from your IDE. Ask complex questions like, "How many orders were
  delivered in 2024, and what items were in them?" without writing any SQL.
• Automate Database Management: Simply describe your data needs, and let the
  AI assistant manage your database for you. It can handle generating queries,
  creating tables, adding indexes, and more.
• Generate Context-Aware Code: Empower your AI assistant to generate
  application code and tests with a deep understanding of your real-time
  database schema. This accelerates the development cycle by ensuring the
  generated code is directly usable.
• Slash Development Overhead: Radically reduce the time spent on manual
  setup and boilerplate. MCP Toolbox helps streamline lengthy database
  configurations, repetitive code, and error-prone schema migrations.

Learn how to connect your AI tools (IDEs) to Toolbox using MCP.

General Architecture

Toolbox sits between your application's orchestration framework and your
database, providing a control plane that is used to modify, distribute, or
invoke tools. It simplifies the management of your tools by providing you with a
centralized location to store and update tools, allowing you to share tools
between agents and applications and update those tools without necessarily
redeploying your application.

Getting Started

Installing the server

For the latest version, check the releases page and use the
following instructions for your OS and CPU architecture.

<details open> <summary>Binary</summary>

To install Toolbox as a binary:

<!-- {x-release-please-start-version} -->

# see releases page for other versions
export VERSION=0.8.0
curl -O https://storage.googleapis.com/genai-toolbox/v$VERSION/linux/amd64/toolbox
chmod +x toolbox

</details> <details> <summary>Container image</summary> You can also install Toolbox as a container:

# see releases page for other versions
export VERSION=0.8.0
docker pull us-central1-docker.pkg.dev/database-toolbox/toolbox/toolbox:$VERSION

</details> <details> <summary>Compile from source</summary>

To install from source, ensure you have the latest version of
Go installed, and then run the following command:

go install github.com/googleapis/genai-toolbox@v0.8.0

<!-- {x-release-please-end} --> </details>

Running the server

Configure a tools.yaml to define your tools, and then
execute toolbox to start the server:

./toolbox --tools-file "tools.yaml"

You can use toolbox help for a full list of flags! To stop the server, send a
terminate signal (ctrl+c on most platforms).

For more detailed documentation on deploying to different environments, check
out the resources in the How-to
section

Integrating your application

Once your server is up and running, you can load the tools into your
application. See below the list of Client SDKs for using various frameworks:

<details open> <summary>Python</summary> <br> <blockquote> <details open> <summary>Core</summary>

1. Install Toolbox Core SDK:

pip install toolbox-core

1. Load tools:

from toolbox_core import ToolboxClient

# update the url to point to your server
async with ToolboxClient("http://127.0.0.1:5000") as client:

# these tools can be passed to your application!
tools = await client.load_toolset("toolset_name")

For more detailed instructions on using the Toolbox Core SDK, see the
project's README.

</details> <details> <summary>LangChain / LangGraph</summary>

1. Install Toolbox LangChain SDK:

pip install toolbox-langchain

1. Load tools:

from toolbox_langchain import ToolboxClient

# update the url to point to your server
async with ToolboxClient("http://127.0.0.1:5000") as client:

# these tools can be passed to your application!
tools = client.load_toolset()

For more detailed instructions on using the Toolbox LangChain SDK, see the
project's README.

</details> <details> <summary>LlamaIndex</summary>

1. Install Toolbox Llamaindex SDK:

pip install toolbox-llamaindex

1. Load tools:

from toolbox_llamaindex import ToolboxClient

# update the url to point to your server
async with ToolboxClient("http://127.0.0.1:5000") as client:

# these tools can be passed to your application!
tools = client.load_toolset()

For more detailed instructions on using the Toolbox Llamaindex SDK, see the
project's README.

</details> </details> </blockquote> <details> <summary>Javascript/Typescript</summary> <br> <blockquote> <details open> <summary>Core</summary>

1. Install Toolbox Core SDK:

npm install @toolbox-sdk/core

1. Load tools:

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

// update the url to point to your server
const URL = 'http://127.0.0.1:5000';
let client = new ToolboxClient(URL);

// these tools can be passed to your application!
const tools = await client.loadToolset('toolsetName');

For more detailed instructions on using the Toolbox Core SDK, see the
project's README.

</details> <details> <summary>LangChain / LangGraph</summary>

1. Install Toolbox Core SDK:

npm install @toolbox-sdk/core

2. Load tools:

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

// update the url to point to your server
const URL = 'http://127.0.0.1:5000';
let client = new ToolboxClient(URL);

// these tools can be passed to your application!
const toolboxTools = await client.loadToolset('toolsetName');

// Define the basics of the tool: name, description, schema and core logic
const getTool = (toolboxTool) => tool(currTool, {
name: toolboxTool.getName(),
description: toolboxTool.getDescription(),
schema: toolboxTool.getParamSchema()
});

// Use these tools in your Langchain/Langraph applications
const tools = toolboxTools.map(getTool);

</details> <details> <summary>Genkit</summary>

1. Install Toolbox Core SDK:

npm install @toolbox-sdk/core

2. Load tools:

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

// Initialise genkit
const ai = genkit({
plugins: [
googleAI({
apiKey: process.env.GEMINI_API_KEY || process.env.GOOGLE_API_KEY
})
],
model: googleAI.model('gemini-2.0-flash'),
});

// update the url to point to your server
const URL = 'http://127.0.0.1:5000';
let client = new ToolboxClient(URL);

// these tools can be passed to your application!
const toolboxTools = await client.loadToolset('toolsetName');

// Define the basics of the tool: name, description, schema and core logic
const getTool = (toolboxTool) => ai.defineTool({
name: toolboxTool.getName(),
description: toolboxTool.getDescription(),
schema: toolboxTool.getParamSchema()
}, toolboxTool)

// Use these tools in your Genkit applications
const tools = toolboxTools.map(getTool);

</details> </details> </blockquote> </details>

Configuration

The primary way to configure Toolbox is through the tools.yaml file. If you
have multiple files, you can tell toolbox which to load with the --tools-file tools.yaml flag.

You can find more detailed reference documentation to all resource types in the
Resources.

Sources

The sources section of your tools.yaml defines what data sources your
Toolbox should have access to. Most tools will have at least one source to
execute against.

sources:
 my-pg-source:
 kind: postgres
 host: 127.0.0.1
 port: 5432
 database: toolbox_db
 user: toolbox_user
 password: my-password

For more details on configuring different types of sources, see the
Sources.

Tools

The tools section of a tools.yaml define the actions an agent can take: what
kind of tool it is, which source(s) it affects, what parameters it uses, etc.

tools:
 search-hotels-by-name:
 kind: postgres-sql
 source: my-pg-source
 description: Search for hotels based on name.
 parameters:
 - name: name
 type: string
 description: The name of the hotel.
 statement: SELECT * FROM hotels WHERE name ILIKE '%' || $1 || '%';

For more details on configuring different types of tools, see the
Tools.

Toolsets

The toolsets section of your tools.yaml allows you to define groups of tools
that you want to be able to load together. This can be useful for defining
different groups based on agent or application.

toolsets:
 my_first_toolset:
 - my_first_tool
 - my_second_tool
 my_second_toolset:
 - my_second_tool
 - my_third_tool

You can load toolsets by name:

# This will load all tools
all_tools = client.load_toolset()

# This will only load the tools listed in 'my_second_toolset'
my_second_toolset = client.load_toolset("my_second_toolset")

Versioning

This project uses semantic versioning, including a
MAJOR.MINOR.PATCH version number that increments with:

• MAJOR version when we make incompatible API changes
• MINOR version when we add functionality in a backward compatible manner
• PATCH version when we make backward compatible bug fixes

The public API that this applies to is the CLI associated with Toolbox, the
interactions with official SDKs, and the definitions in the tools.yaml file.

Contributing

Contributions are welcome. Please, see the CONTRIBUTING
to get started.

Please note that this project is released with a Contributor Code of Conduct.
By participating in this project you agree to abide by its terms. See
Contributor Code of Conduct for more information.