MCP Toolbox For Databases

MCP Toolbox For Databases

What is MCP Toolbox For Databases about?

MCP Toolbox For Databases delivers an open‑source MCP server that centralizes the definition, deployment, and execution of database‑related tools. It handles low‑level concerns such as connection pooling, secure authentication, metrics, and tracing, allowing developers to focus on the logic of their AI‑driven tools.

How to use MCP Toolbox For Databases?

1. Install the server – choose a binary, Docker image, Homebrew, or compile from source.
2. Create a tools.yaml – describe data sources, individual tools (SQL statements, parameters, etc.), and group them into toolsets.
3. Start the server – run toolbox --tools-file "tools.yaml" (or the equivalent Docker command).
4. Load tools from a client – use one of the provided SDKs (Python, JavaScript/TypeScript, Go) to connect to http://localhost:5000 and load a toolset.
5. Integrate with agents – pass the loaded tools to LangChain, Genkit, LlamaIndex, OpenAI, etc., enabling natural‑language interaction with your database.

Key features of MCP Toolbox For Databases

• One‑click server with binary, container, and Homebrew support.
• Declarative tools.yaml for sources, tools, and toolsets.
• Dynamic reloading of tool definitions without restarting the server.
• Built‑in observability via OpenTelemetry metrics and tracing.
• Secure connection handling (pooling, authentication).
• Multi‑language SDKs (Python, JS/TS, Go) including LangChain, GenKit, LlamaIndex, OpenAI, and more.
• Extensible architecture allowing custom source kinds and tool behaviours.

Use cases of MCP Toolbox For Databases

• AI‑augmented IDE assistants – query databases in plain English directly from development environments.
• Automated reporting – generate and run complex SQL reports on demand via natural‑language prompts.
• Schema‑aware code generation – produce application code and tests that reflect the current database schema.
• Self‑service data portals – expose curated toolsets to non‑technical users for safe data exploration.
• DevOps automation – let AI agents manage migrations, index creation, and routine maintenance tasks.

FAQ from the MCP Toolbox For Databases

Q: Is the server production‑ready? A: The project is in beta (pre‑1.0). Breaking changes may occur until the first stable release.

Q: Which databases are supported? A: The sources section supports PostgreSQL out‑of‑the‑box; additional source kinds can be added via extensions.

Q: How do I update tools without redeploying my application? A: Edit tools.yaml; the server automatically reloads changes unless the --disable-reload flag is used.

Q: Do I need to run the server inside Kubernetes? A: Not required; you can run it locally as a binary, Docker container, or via Homebrew. Container images are also suitable for Kubernetes deployments.

Q: Where can I find full documentation? A: Comprehensive docs are hosted at https://googleapis.github.io/genai-toolbox/.

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.

Table of Contents

• Why Toolbox?
• General Architecture
• Getting Started
  • Installing the server
  • Running the server
    • Homebrew Users
  • Integrating your application
• Configuration
  • Sources
  • Tools
  • Toolsets
• Versioning
  • Pre-1.0.0 Versioning
  • Post-1.0.0 Versioning
• Contributing
• Community

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.

To install Toolbox as a binary:

# see releases page for other versions
export VERSION=0.13.0
curl -O https://storage.googleapis.com/genai-toolbox/v$VERSION/linux/amd64/toolbox
chmod +x toolbox

# see releases page for other versions
export VERSION=0.13.0
docker pull us-central1-docker.pkg.dev/database-toolbox/toolbox/toolbox:$VERSION

To install Toolbox using Homebrew on macOS or Linux:

brew install mcp-toolbox

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@v0.13.0

Running the server

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

To run Toolbox from binary:

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

ⓘ NOTE:
Toolbox enables dynamic reloading by default. To disable, use the --disable-reload flag.

To run the server after pulling the container image:

export VERSION=0.11.0 # Use the version you pulled
docker run -p 5000:5000 \
-v $(pwd)/tools.yaml:/app/tools.yaml \
us-central1-docker.pkg.dev/database-toolbox/toolbox/toolbox:$VERSION \
--tools-file "/app/tools.yaml"

ⓘ NOTE:
The -v flag mounts your local tools.yaml into the container, and -p maps the container's port 5000 to your host's port 5000.

To run the server directly from source, navigate to the project root directory and run:

go run .

ⓘ NOTE:
This command runs the project from source, and is more suitable for development and testing. It does not compile a binary into your $GOPATH. If you want to compile a binary instead, refer the Developer Documentation.

If you installed Toolbox using Homebrew, the toolbox binary is available in your system path. You can start the server with the same command:

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:

1. Install Toolbox Core SDK:

   pip install toolbox-core
   

2. 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.

1. Install Toolbox LangChain SDK:

   pip install toolbox-langchain
   

2. 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.

1. Install Toolbox Llamaindex SDK:

   pip install toolbox-llamaindex
   

2. 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.

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 tools = await client.loadToolset('toolsetName');
   

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

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);
   

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);
   

1. Install Toolbox Go SDK:

   go get github.com/googleapis/mcp-toolbox-sdk-go
   

2. Load tools:

   package main
   
   import (
     "github.com/googleapis/mcp-toolbox-sdk-go/core"
     "context"
   )
   
   func main() {
     // Make sure to add the error checks
     // update the url to point to your server
     URL := "http://127.0.0.1:5000";
     ctx := context.Background()
   
     client, err := core.NewToolboxClient(URL)
   
     // Framework agnostic tools
     tools, err := client.LoadToolset("toolsetName", ctx)
   }
   

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

1. Install Toolbox Go SDK:

   go get github.com/googleapis/mcp-toolbox-sdk-go
   

2. Load tools:

   package main
   
   import (
     "context"
     "encoding/json"
   
     "github.com/googleapis/mcp-toolbox-sdk-go/core"
     "github.com/tmc/langchaingo/llms"
   )
   
   func main() {
     // Make sure to add the error checks
     // update the url to point to your server
     URL := "http://127.0.0.1:5000"
     ctx := context.Background()
   
     client, err := core.NewToolboxClient(URL)
   
     // Framework agnostic tool
     tool, err := client.LoadTool("toolName", ctx)
   
     // Fetch the tool's input schema
     inputschema, err := tool.InputSchema()
   
     var paramsSchema map[string]any
     _ = json.Unmarshal(inputschema, &paramsSchema)
   
     // Use this tool with LangChainGo
     langChainTool := llms.Tool{
       Type: "function",
       Function: &llms.FunctionDefinition{
         Name:        tool.Name(),
         Description: tool.Description(),
         Parameters:  paramsSchema,
       },
     }
   }
   
   

1. Install Toolbox Go SDK:

   go get github.com/googleapis/mcp-toolbox-sdk-go
   

2. Load tools:

   package main
   import (
     "context"
     "encoding/json"
   
     "github.com/firebase/genkit/go/ai"
     "github.com/firebase/genkit/go/genkit"
     "github.com/googleapis/mcp-toolbox-sdk-go/core"
     "github.com/googleapis/mcp-toolbox-sdk-go/tbgenkit"
     "github.com/invopop/jsonschema"
   )
   
   func main() {
     // Make sure to add the error checks
     // Update the url to point to your server
     URL := "http://127.0.0.1:5000"
     ctx := context.Background()
     g, err := genkit.Init(ctx)
   
     client, err := core.NewToolboxClient(URL)
   
     // Framework agnostic tool
     tool, err := client.LoadTool("toolName", ctx)
   
     // Convert the tool using the tbgenkit package
     // Use this tool with Genkit Go
     genkitTool, err := tbgenkit.ToGenkitTool(tool, g)
     if err != nil {
       log.Fatalf("Failed to convert tool: %v\n", err)
     }
   }
   

1. Install Toolbox Go SDK:

   go get github.com/googleapis/mcp-toolbox-sdk-go
   

2. Load tools:

   package main
   
   import (
     "context"
     "encoding/json"
   
     "github.com/googleapis/mcp-toolbox-sdk-go/core"
     "google.golang.org/genai"
   )
   
   func main() {
     // Make sure to add the error checks
     // Update the url to point to your server
     URL := "http://127.0.0.1:5000"
     ctx := context.Background()
   
     client, err := core.NewToolboxClient(URL)
   
     // Framework agnostic tool
     tool, err := client.LoadTool("toolName", ctx)
   
     // Fetch the tool's input schema
     inputschema, err := tool.InputSchema()
   
     var schema *genai.Schema
     _ = json.Unmarshal(inputschema, &schema)
   
     funcDeclaration := &genai.FunctionDeclaration{
       Name:        tool.Name(),
       Description: tool.Description(),
       Parameters:  schema,
     }
   
     // Use this tool with Go GenAI
     genAITool := &genai.Tool{
       FunctionDeclarations: []*genai.FunctionDeclaration{funcDeclaration},
     }
   }
   

1. Install Toolbox Go SDK:

   go get github.com/googleapis/mcp-toolbox-sdk-go
   

2. Load tools:

   package main
   
   import (
     "context"
     "encoding/json"
   
     "github.com/googleapis/mcp-toolbox-sdk-go/core"
     openai "github.com/openai/openai-go"
   )
   
   func main() {
     // Make sure to add the error checks
     // Update the url to point to your server
     URL := "http://127.0.0.1:5000"
     ctx := context.Background()
   
     client, err := core.NewToolboxClient(URL)
   
     // Framework agnostic tool
     tool, err := client.LoadTool("toolName", ctx)
   
     // Fetch the tool's input schema
     inputschema, err := tool.InputSchema()
   
     var paramsSchema openai.FunctionParameters
     _ = json.Unmarshal(inputschema, &paramsSchema)
   
     // Use this tool with OpenAI Go
     openAITool := openai.ChatCompletionToolParam{
       Function: openai.FunctionDefinitionParam{
         Name:        tool.Name(),
         Description: openai.String(tool.Description()),
         Parameters:  paramsSchema,
       },
     }
   
   }
   

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 (MAJOR.MINOR.PATCH). Since the project is in a pre-release stage (version 0.x.y), we follow the standard conventions for initial development:

Pre-1.0.0 Versioning

While the major version is 0, the public API should be considered unstable. The version will be incremented as follows:

• 0.MINOR.PATCH: The MINOR version is incremented when we add new functionality or make breaking, incompatible API changes.
• 0.MINOR.PATCH: The PATCH version is incremented for backward-compatible bug fixes.

Post-1.0.0 Versioning

Once the project reaches a stable 1.0.0 release, the versioning will follow the more common convention:

• MAJOR.MINOR.PATCH: Incremented for incompatible API changes.
• MAJOR.MINOR.PATCH: Incremented for new, backward-compatible functionality.
• MAJOR.MINOR.PATCH: Incremented for 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.

Community

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