### Go Quickstart for MCP Toolbox

Source: https://googleapis.github.io/genai-toolbox/getting-started/configure/

Provides a quickstart guide for using the MCP Toolbox with Go, likely involving setup and basic usage for local development.

```Go
package main

import (
	"fmt"
	"github.com/google/generative-ai-go/genai"
	"context"
)

func main() {
	ctx := context.Background()
	// Example usage (conceptual)
	// client, err := genai.NewClient(ctx, "your-project-id")
	// if err != nil {
	// 	log.Fatal(err)
	// }
	// defer client.Close()

	// fmt.Println("MCP Toolbox Go Quickstart")
}
```

--------------------------------

### Quickstart Streaming Example (Java)

Source: https://google.github.io/adk-docs

This snippet provides a quickstart guide for streaming functionality using Java within the Agent Development Kit. It outlines the necessary steps to get started with streaming agents.

```Java
This is a placeholder for actual Java code. The documentation indicates a quickstart for streaming agents in Java.
```

--------------------------------

### Example Store Quickstart

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/manage/overview

A quickstart guide to navigating and utilizing the Example Store. Learn how to find, download, and run code examples relevant to your projects.

```Bash
# Navigate to the Example Store directory (assuming it's cloned locally)
# cd vertex-ai-examples

# Browse available examples
# ls -R examples/

# Example: Running a Hugging Face model example
# cd examples/huggingface/
# python run_model.py --model_name "distilbert-base-uncased"

# Example: Running an Agent Engine quickstart
# cd examples/agent-engine/quickstart/
# python main.py

echo "Example Store quickstart guide complete. Start exploring examples."
```

--------------------------------

### Setup LiteLLM Dashboard Frontend

Source: https://github.com/BerriAI/litellm

Installs dependencies and starts the LiteLLM dashboard, a UI for interacting with the proxy.

```bash
cd ui/litellm-dashboard
```

```bash
npm install
```

```bash
npm run dev
```

--------------------------------

### Example Store Quickstart

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/live-api

A quickstart guide to navigating and utilizing the Example Store. Learn how to find, download, and run code examples relevant to your projects.

```Bash
# Navigate to the Example Store directory (assuming it's cloned locally)
# cd vertex-ai-examples

# Browse available examples
# ls -R examples/

# Example: Running a Hugging Face model example
# cd examples/huggingface/
# python run_model.py --model_name "distilbert-base-uncased"

# Example: Running an Agent Engine quickstart
# cd examples/agent-engine/quickstart/
# python main.py

echo "Example Store quickstart guide complete. Start exploring examples."
```

--------------------------------

### Quickstart Streaming Example (Python)

Source: https://google.github.io/adk-docs

This snippet provides a quickstart guide for streaming functionality using Python within the Agent Development Kit. It outlines the necessary steps to get started with streaming agents.

```Python
This is a placeholder for actual Python code. The documentation indicates a quickstart for streaming agents in Python.
```

--------------------------------

### Agent Engine Runtime Quickstart

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/quickstart

Quickstart guide for the Agent Engine runtime environment. This covers the initial setup and basic usage of the runtime for executing agents.

```Python
# Example code for Agent Engine runtime quickstart
# This might involve setting up the environment and running a basic agent.

# Placeholder for runtime quickstart script
print("Agent Engine runtime quickstart.")
# Add specific runtime commands here
```

--------------------------------

### Go Quickstart for GenAI Toolbox

Source: https://googleapis.github.io/genai-toolbox/getting-started

This guide details the local installation and usage of the GenAI Toolbox with Go. It outlines dependencies such as PostgreSQL and compatibility with Go-specific AI frameworks like LangChain Go, GenkitGo, Go GenAI, and OpenAI Go.

```Go
go get github.com/googleapis/go-genai
# Further setup instructions would follow here, referencing the Go SDK.
```

--------------------------------

### BigQuery Local Quickstart

Source: https://googleapis.github.io/genai-toolbox/getting-started

A quickstart guide for setting up and using BigQuery locally. Covers initial configuration and basic operations.

```Markdown
## Quickstart (Local with BigQuery)

This guide helps you get started with BigQuery in a local environment. Follow these steps to set up your connection and run your first query.
```

--------------------------------

### Quickstart with BigQuery (Local)

Source: https://googleapis.github.io/genai-toolbox/getting-started/introduction/

Provides a quickstart guide for using BigQuery locally. This sample demonstrates basic BigQuery interactions.

```Python
from genai_toolbox.samples.bigquery import local_quickstart

# Example usage:
# local_quickstart.run_quickstart()
```

--------------------------------

### Looker Gemini MCP Quickstart

Source: https://googleapis.github.io/genai-toolbox/getting-started

A quickstart guide for using Looker with Gemini-CLI in the Managed Cloud Platform (MCP). Covers setup and integration.

```Markdown
## Quickstart (MCP with Looker and Gemini-CLI)

This guide provides a step-by-step walkthrough for using Looker with Gemini-CLI in an MCP environment. Get started quickly with this setup.
```

--------------------------------

### Quickstart Prompts in TypeScript

Source: https://arize.com/docs/phoenix

This section offers a quickstart guide for implementing prompts using TypeScript. It is expected to detail the initial setup and fundamental operations for prompt engineering in a TypeScript environment.

```TypeScript
import * as px from '@phoenix/core';

// Example usage for prompts in TypeScript
// ... (code will be here)
```

--------------------------------

### Quickstart with BigQuery (Local)

Source: https://googleapis.github.io/genai-toolbox/getting-started/configure/

Provides a quickstart guide for using BigQuery locally. This sample demonstrates basic BigQuery interactions.

```Python
from genai_toolbox.samples.bigquery import local_quickstart

# Example usage:
# local_quickstart.run_quickstart()
```

--------------------------------

### Getting Started with AlloyDB AI-NL Tool

Source: https://googleapis.github.io/genai-toolbox/getting-started/introduction/

Guides users on getting started with the AlloyDB AI-NL tool. This sample focuses on natural language interactions with AlloyDB.

```Python
from genai_toolbox.samples.alloydb import ai_nl

# Example usage:
# ai_nl.run_example()
```

--------------------------------

### Quickstart with BigQuery (Local)

Source: https://googleapis.github.io/genai-toolbox/resources/tools/

Provides a quickstart guide for using BigQuery locally. This sample demonstrates basic BigQuery interactions.

```Python
from genai_toolbox.samples.bigquery import local_quickstart

# Example usage:
# local_quickstart.run_quickstart()
```

--------------------------------

### Go Quickstart for MCP Toolbox

Source: https://googleapis.github.io/genai-toolbox/resources/tools/

Provides a quickstart guide for using the MCP Toolbox with Go. This likely involves setting up the environment and making initial connections to databases.

```Go
package main

import (
	"fmt"
	"mcp-toolbox/client"
)

func main() {
	// Example usage (conceptual)
	cli, err := client.NewClient()
	if err != nil {
		// Handle error
	}

	response, err := cli.Query("SELECT * FROM my_table")
	if err != nil {
		// Handle error
	}

	fmt.Println(response)
}
```

--------------------------------

### Quickstart with BigQuery (MCP)

Source: https://googleapis.github.io/genai-toolbox/getting-started/introduction/

Provides a quickstart guide for using BigQuery with MCP (Multi-Cloud Platform). This sample demonstrates cloud-based BigQuery interactions.

```Python
from genai_toolbox.samples.bigquery import mcp_quickstart

# Example usage:
# mcp_quickstart.run_quickstart()
```

--------------------------------

### Python Quickstart for MCP Toolbox

Source: https://googleapis.github.io/genai-toolbox/getting-started/configure/

Provides a quickstart guide for using the MCP Toolbox with Python, likely involving setup and basic usage for local development.

```Python
import mcp_toolbox

# Example usage (conceptual)
# config = mcp_toolbox.load_config('config.yaml')
# client = mcp_toolbox.connect(config)
# result = client.query('SELECT * FROM my_table')
# print(result)
```

--------------------------------

### JavaScript Quickstart for GenAI Toolbox

Source: https://googleapis.github.io/genai-toolbox/getting-started

This guide covers the local setup of the GenAI Toolbox using JavaScript. It includes prerequisites like PostgreSQL and integration with orchestration frameworks like LangChain, GenkitJS, LlamaIndex, and GoogleGenAI.

```JavaScript
npm install @google/generative-ai
# Further setup instructions would follow here, referencing the JS SDK.
```

--------------------------------

### Getting Started with AlloyDB AI-NL Tool

Source: https://googleapis.github.io/genai-toolbox/getting-started/configure/

Guides users on getting started with the AlloyDB AI-NL tool. This sample focuses on natural language interactions with AlloyDB.

```Python
from genai_toolbox.samples.alloydb import ai_nl

# Example usage:
# ai_nl.run_example()
```

--------------------------------

### JavaScript Quickstart for MCP Toolbox

Source: https://googleapis.github.io/genai-toolbox/getting-started/configure/

Provides a quickstart guide for using the MCP Toolbox with JavaScript, likely involving setup and basic usage for local development.

```JavaScript
import mcpToolbox from 'mcp-toolbox';

// Example usage (conceptual)
// async function run() {
//   const config = await mcpToolbox.loadConfig('config.json');
//   const client = await mcpToolbox.connect(config);
//   const result = await client.query('SELECT * FROM my_table');
//   console.log(result);
// }
// run();
```

--------------------------------

### Quickstart for Agent Development Kit (ADK)

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/grounding/grounding-search-suggestions

This quickstart guide provides the essential steps to begin using the Agent Development Kit (ADK). It covers setting up your environment and creating your first agent.

```Python
# Conceptual Python code for ADK quickstart
# from google.cloud import aiplatform
# from vertexai.generative_models import GenerativeModel, Part

# aiplatform.init(project='your-project-id', location='us-central1')

# # Initialize a generative model
# model = GenerativeModel("gemini-1.0-pro")

# # Define a simple agent function (conceptual)
# def my_agent_function(user_input):
#     response = model.generate_content(f"User query: {user_input}")
#     return response.text

# # Example usage
# user_query = "What is the weather today?"
# agent_response = my_agent_function(user_query)
# print(f"Agent response: {agent_response}")

# Note: This is a high-level example. Actual ADK usage involves more complex configurations and interactions.
```

--------------------------------

### Quickstart with Looker and Gemini-CLI (MCP)

Source: https://googleapis.github.io/genai-toolbox/getting-started/introduction/

Provides a quickstart guide for using Looker and Gemini-CLI with MCP (Multi-Cloud Platform). This sample integrates AI tools with Looker.

```Python
from genai_toolbox.samples.looker import looker_gemini

# Example usage:
# looker_gemini.run_quickstart()
```

--------------------------------

### Quickstart for Agent Development Kit (ADK)

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/manage/overview

Get started with the Agent Development Kit (ADK) for building intelligent agents. This quickstart guide provides the essential steps to set up and begin developing your first agent.

```Python
from google.cloud import aiplatform
from vertexai.preview.generative_models import GenerativeModel, Part

# Initialize Vertex AI
aiplatform.init(project="your-project-id", location="your-region")

# Load a generative model
model = GenerativeModel("gemini-1.0-pro")

# Example of agent interaction (conceptual)
# response = model.generate_content("Create a travel itinerary for Paris.")
# print(response.text)

print("ADK quickstart setup complete. Ready for agent development.")
```

--------------------------------

### Quickstart for Agent Development Kit (ADK)

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/live-api

Get started with the Agent Development Kit (ADK) for building intelligent agents. This quickstart guide provides the essential steps to set up and begin developing your first agent.

```Python
from google.cloud import aiplatform
from vertexai.preview.generative_models import GenerativeModel, Part

# Initialize Vertex AI
aiplatform.init(project="your-project-id", location="your-region")

# Load a generative model
model = GenerativeModel("gemini-1.0-pro")

# Example of agent interaction (conceptual)
# response = model.generate_content("Create a travel itinerary for Paris.")
# print(response.text)

print("ADK quickstart setup complete. Ready for agent development.")
```

--------------------------------

### Quickstart with AlloyDB (MCP)

Source: https://googleapis.github.io/genai-toolbox/getting-started/introduction/

Provides a quickstart guide for using AlloyDB with MCP (Multi-Cloud Platform). This sample demonstrates cloud-based AlloyDB interactions.

```Python
from genai_toolbox.samples.alloydb import mcp_quickstart

# Example usage:
# mcp_quickstart.run_quickstart()
```

--------------------------------

### Install Go SDK

Source: https://github.com/googleapis/genai-toolbox

Command to install the Toolbox Go SDK using the go get command. This is the initial step to start using the SDK in a Go project.

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

--------------------------------

### Quickstart with BigQuery (MCP)

Source: https://googleapis.github.io/genai-toolbox/getting-started/configure/

Provides a quickstart guide for using BigQuery with MCP (Multi-Cloud Platform). This sample demonstrates cloud-based BigQuery interactions.

```Python
from genai_toolbox.samples.bigquery import mcp_quickstart

# Example usage:
# mcp_quickstart.run_quickstart()
```

--------------------------------

### Quickstart for Agent Development Kit (ADK)

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/overview

This quickstart guide provides the essential steps to begin using the Agent Development Kit (ADK). It covers setting up your environment and creating your first agent.

```Python
# Conceptual Python code for ADK quickstart
# from google.cloud import aiplatform
# from vertexai.generative_models import GenerativeModel, Part

# aiplatform.init(project='your-project-id', location='us-central1')

# # Initialize a generative model
# model = GenerativeModel("gemini-1.0-pro")

# # Define a simple agent function (conceptual)
# def my_agent_function(user_input):
#     response = model.generate_content(f"User query: {user_input}")
#     return response.text

# # Example usage
# user_query = "What is the weather today?"
# agent_response = my_agent_function(user_query)
# print(f"Agent response: {agent_response}")

# Note: This is a high-level example. Actual ADK usage involves more complex configurations and interactions.
```

--------------------------------

### Example Store Quickstart

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/configure-safety-attributes

A quickstart guide to navigating and utilizing the Example Store. Learn how to find, download, and run code examples relevant to your projects.

```Bash
# Navigate to the Example Store directory (assuming it's cloned locally)
# cd vertex-ai-examples

# Browse available examples
# ls -R examples/

# Example: Running a Hugging Face model example
# cd examples/huggingface/
# python run_model.py --model_name "distilbert-base-uncased"

# Example: Running an Agent Engine quickstart
# cd examples/agent-engine/quickstart/
# python main.py

echo "Example Store quickstart guide complete. Start exploring examples."
```

--------------------------------

### Running LiteLLM in Developer Mode - Backend Setup

Source: https://github.com/BerriAI/litellm

Steps to set up the backend for LiteLLM development, including creating a virtual environment, activating it, installing dependencies, and starting the proxy backend.

```bash
# (In root) create virtual environment
python -m venv .venv
# Activate virtual environment
source .venv/bin/activate
# Install dependencies
pip install -e ".[all]"
# Start proxy backend
python3 /path/to/litellm/proxy_cli.py
```

--------------------------------

### Python Quickstart for MCP Toolbox

Source: https://googleapis.github.io/genai-toolbox/resources/tools/

Provides a quickstart guide for using the MCP Toolbox with Python. This likely involves setting up the environment and making initial connections to databases.

```Python
import mcp_toolbox

# Example usage (conceptual)
client = mcp_toolbox.Client()
response = client.query("SELECT * FROM my_table")
print(response)
```

--------------------------------

### Quickstart with BigQuery (MCP)

Source: https://googleapis.github.io/genai-toolbox/resources/tools/

Provides a quickstart guide for using BigQuery with MCP (Multi-Cloud Platform). This sample demonstrates cloud-based BigQuery interactions.

```Python
from genai_toolbox.samples.bigquery import mcp_quickstart

# Example usage:
# mcp_quickstart.run_quickstart()
```

--------------------------------

### Quickstart with Looker and Gemini-CLI (MCP)

Source: https://googleapis.github.io/genai-toolbox/getting-started/configure/

Provides a quickstart guide for using Looker and Gemini-CLI with MCP (Multi-Cloud Platform). This sample integrates AI tools with Looker.

```Python
from genai_toolbox.samples.looker import looker_gemini

# Example usage:
# looker_gemini.run_quickstart()
```

--------------------------------

### BigQuery MCP Quickstart

Source: https://googleapis.github.io/genai-toolbox/getting-started

A quickstart guide for using BigQuery with the Managed Cloud Platform (MCP). Focuses on deployment and integration within the MCP environment.

```Markdown
## Quickstart (MCP with BigQuery)

This guide covers the essentials for using BigQuery within the Managed Cloud Platform (MCP). Learn how to configure and interact with BigQuery in this setup.
```

--------------------------------

### Quickstart with AlloyDB (MCP)

Source: https://googleapis.github.io/genai-toolbox/getting-started/configure/

Provides a quickstart guide for using AlloyDB with MCP (Multi-Cloud Platform). This sample demonstrates cloud-based AlloyDB interactions.

```Python
from genai_toolbox.samples.alloydb import mcp_quickstart

# Example usage:
# mcp_quickstart.run_quickstart()
```

--------------------------------

### Set up Environment & Install ADK (Python)

Source: https://google.github.io/adk-docs/get-started/quickstart/

This snippet shows how to create and activate a Python virtual environment and install the google-adk package using pip. It's a prerequisite for setting up the ADK.

```bash
# Create
python -m venv .venv
# Activate (each new terminal)
# macOS/Linux: source .venv/bin/activate
# Windows CMD: .venv\Scripts\activate.bat
# Windows PowerShell: .venv\Scripts\Activate.ps1
```

```bash
pip install google-adk
```

--------------------------------

### Running LiteLLM in Developer Mode - Frontend Setup

Source: https://github.com/BerriAI/litellm

Instructions for setting up and running the LiteLLM frontend dashboard, including navigating to the directory, installing npm dependencies, and starting the development server.

```bash
# Navigate to ui/litellm-dashboard
cd ui/litellm-dashboard
# Install dependencies
npm install
# Run dev server
npm run dev
```

--------------------------------

### JavaScript Quickstart for MCP Toolbox

Source: https://googleapis.github.io/genai-toolbox/resources/tools/

Provides a quickstart guide for using the MCP Toolbox with JavaScript. This likely involves setting up the environment and making initial connections to databases.

```JavaScript
const mcpToolbox = require('mcp-toolbox');

// Example usage (conceptual)
const client = new mcpToolbox.Client();
client.query('SELECT * FROM my_table').then(response => {
  console.log(response);
});
```

--------------------------------

### Quickstart with Looker and Gemini-CLI (MCP)

Source: https://googleapis.github.io/genai-toolbox/resources/tools/

Provides a quickstart guide for using Looker and Gemini-CLI with MCP (Multi-Cloud Platform). This sample integrates AI tools with Looker.

```Python
from genai_toolbox.samples.looker import looker_gemini

# Example usage:
# looker_gemini.run_quickstart()
```

--------------------------------

### ADK Quickstart

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/quickstart

Quickstart guide for the Agent Development Kit (ADK). This provides a rapid introduction to setting up and using the ADK for agent creation.

```Python
from google.generativeai.agent import Agent

# Initialize the agent
agent = Agent(name="MyFirstAgent")

# Add tools or functionalities to the agent
# agent.add_tool(...)

# Run the agent
# agent.run("What is the weather like today?")

print("ADK Quickstart setup complete.")
```

--------------------------------

### Quickstart with AlloyDB (MCP)

Source: https://googleapis.github.io/genai-toolbox/resources/tools/

Provides a quickstart guide for using AlloyDB with MCP (Multi-Cloud Platform). This sample demonstrates cloud-based AlloyDB interactions.

```Python
from genai_toolbox.samples.alloydb import mcp_quickstart

# Example usage:
# mcp_quickstart.run_quickstart()
```

--------------------------------

### Navigate to Quickstart Guide

Source: https://arize.com/docs/ax

This JavaScript snippet shows how to create a link to the Arize AI quickstart guide. It utilizes Next.js routing and includes event tracking for link clicks.

```javascript
self.__next_f.push([1,"7e:[\"$\",\"$L76\",\"FhElgGnZ64IM\",{\"href\":\"/docs/ax/develop/quickstart\",\"classNames\":[\"RecordCardStyles\"]...])
```

--------------------------------

### ADK Java Quickstart (Streaming)

Source: https://google.github.io/adk-docs/

This section provides a quickstart guide for using the Agent Development Kit (ADK) with Java for streaming applications. It covers the initial setup and basic usage patterns for streaming agent interactions.

```Java
get-started/streaming/quickstart-streaming-java/
```

--------------------------------

### Install LiteLLM Proxy

Source: https://docs.litellm.ai/

Command to install the LiteLLM proxy package, which includes the necessary components to run the LLM Gateway. This enables features like cost tracking and rate limiting.

```bash
pip install 'litellm[proxy]'
```

--------------------------------

### Quickstart Prompts in Python

Source: https://arize.com/docs/phoenix

This section provides a quickstart guide for using prompts in Python. It likely covers setting up the environment and basic usage patterns for prompt engineering within a Python application.

```Python
import phoenix as px

# Example usage for prompts in Python
# ... (code will be here)
```

--------------------------------

### Quickstart with Looker (MCP Inspector)

Source: https://googleapis.github.io/genai-toolbox/getting-started/introduction/

Provides a quickstart guide for using Looker with the MCP Inspector. This sample focuses on inspecting Looker data within a cloud environment.

```Python
from genai_toolbox.samples.looker import looker_mcp_inspector

# Example usage:
# looker_mcp_inspector.run_quickstart()
```

--------------------------------

### Vertex AI Image Generation Prompt Guide

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/quickstart

A guide to crafting effective prompts for image generation with Vertex AI. Learn best practices for describing desired image attributes, styles, and content.

```Markdown
## Vertex AI Image Generation Prompt Guide

**Key Elements for Effective Prompts:**

*   **Subject:** Clearly describe the main subject of the image (e.g., 'a cat', 'a futuristic city').
*   **Action/Pose:** Specify what the subject is doing (e.g., 'sitting', 'running', 'glowing').
*   **Setting/Background:** Describe the environment or background (e.g., 'in a forest', 'on a beach', 'against a plain background').
*   **Style:** Indicate the desired artistic style (e.g., 'photorealistic', 'digital art', 'watercolor', 'anime').
*   **Lighting/Mood:** Describe the lighting conditions and overall mood (e.g., 'golden hour lighting', 'dramatic shadows', 'serene atmosphere').
*   **Details:** Add specific details to refine the image (e.g., 'wearing a red hat', 'with intricate patterns').

**Example Prompts:**

*   `A majestic dragon soaring through a stormy sky, digital art, dramatic lighting.`
*   `Photorealistic portrait of an old man with a kind smile, sitting in a cozy armchair, warm lighting.`
*   `A minimalist logo for a coffee shop, featuring a steaming cup, vector art.`
```

--------------------------------

### ADK Python Quickstart (Streaming)

Source: https://google.github.io/adk-docs/

This section provides a quickstart guide for using the Agent Development Kit (ADK) with Python for streaming applications. It covers the initial setup and basic usage patterns for streaming agent interactions.

```Python
get-started/streaming/quickstart-streaming/
```

--------------------------------

### vLLM for LLM Serving (GPU)

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/quickstart

Comprehensive guide to using vLLM for efficient text and multimodal LLM serving on GPUs. This documentation covers setup, configuration, and best practices for high-throughput inference.

```Python
from vllm import LLM, SamplingParams

llm = LLM(model="lmsys/vicuna-7b-v1.5")

sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

outputs = llm.generate("Hello, my name is", sampling_params)

for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```

--------------------------------

### Start LiteLLM Proxy Server (CLI)

Source: https://docs.litellm.ai/

Command to start the LiteLLM proxy server using the command-line interface. This command specifies the model to use and indicates that the proxy is running.

```bash
litellm --model huggingface/bigcode/starcoder
#INFO: Proxy running on http://0.0.0.0:4000
```

--------------------------------

### Quickstart with Looker (MCP Inspector)

Source: https://googleapis.github.io/genai-toolbox/getting-started/configure/

Provides a quickstart guide for using Looker with the MCP Inspector. This sample focuses on inspecting Looker data within a cloud environment.

```Python
from genai_toolbox.samples.looker import looker_mcp_inspector

# Example usage:
# looker_mcp_inspector.run_quickstart()
```

--------------------------------

### Start LiteLLM Proxy Server

Source: https://github.com/BerriAI/litellm

Provides the command to install LiteLLM with proxy support and start the proxy server using the CLI, specifying a model to serve.

```bash
pip install 'litellm[proxy]'

$ litellm --model huggingface/bigcode/starcoder

#INFO: Proxy running on http://0.0.0.0:4000
```

--------------------------------

### Use Example Inferences in Phoenix

Source: https://arize.com/docs/phoenix

This section explains how to utilize example inferences within Phoenix to test and understand the tool's capabilities. It's useful for getting started and validating your setup.

```python
from phoenix.trace import Client

# Assuming you have a Phoenix client initialized
client = Client()

# Example of loading example inferences (specific function would depend on Phoenix API)
# client.load_example_inferences()

```

--------------------------------

### Quickstart with Agent Engine SDK for Memory Bank

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/quickstart

Quickstart guide for using the Agent Engine SDK to interact with the Memory Bank. This covers basic operations for managing memories.

```Python
from google.generativeai.agent import Agent

# Assume agent is initialized
agent = Agent(name="MemoryAgent")

# Interact with Memory Bank using SDK
# agent.memory_bank.save("user_context", {"key": "value"})
# memory = agent.memory_bank.load("user_context")

print("Quickstart with Agent Engine SDK for Memory Bank.")
```

--------------------------------

### LiteLLM Development Setup and Testing

Source: https://github.com/BerriAI/litellm

This command sequence outlines the steps for setting up the LiteLLM development environment, including cloning the repository, installing dependencies, formatting code, linting, and running unit tests.

```bash
git clone
make install-dev
make format
make lint
make test-unit
```

--------------------------------

### Quickstart for Agent Development Kit (ADK)

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/configure-safety-attributes

Get started with the Agent Development Kit (ADK) for building intelligent agents. This quickstart guide provides the essential steps to set up and begin developing your first agent.

```Python
from google.cloud import aiplatform
from vertexai.preview.generative_models import GenerativeModel, Part

# Initialize Vertex AI
aiplatform.init(project="your-project-id", location="your-region")

# Load a generative model
model = GenerativeModel("gemini-1.0-pro")

# Example of agent interaction (conceptual)
# response = model.generate_content("Create a travel itinerary for Paris.")
# print(response.text)

print("ADK quickstart setup complete. Ready for agent development.")
```

--------------------------------

### Quickstart with Looker (MCP Inspector)

Source: https://googleapis.github.io/genai-toolbox/resources/tools/

Provides a quickstart guide for using Looker with the MCP Inspector. This sample focuses on inspecting Looker data within a cloud environment.

```Python
from genai_toolbox.samples.looker import looker_mcp_inspector

# Example usage:
# looker_mcp_inspector.run_quickstart()
```

--------------------------------

### Quickstart with Memory Bank API

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/grounding/grounding-search-suggestions

This quickstart guide demonstrates how to use the Memory Bank feature through API calls. It covers the basic steps for interacting with the Memory Bank to store and retrieve information.

```Python
# Conceptual Python code for Memory Bank quickstart via API
# from google.cloud import aiplatform

# aiplatform.init(project='your-project-id', location='us-central1')

# # Assume Memory Bank is configured and accessible
# # Example: Storing a memory
# memory_id = "user_123_session_abc"
# memory_content = {"last_interaction": "What is the capital of France?", "response": "Paris."}

# # aiplatform.memory_bank.store(id=memory_id, data=memory_content)
# print(f"Stored memory with ID: {memory_id}")

# # Example: Fetching a memory
# # retrieved_memory = aiplatform.memory_bank.get(id=memory_id)
# # print(f"Retrieved memory: {retrieved_memory}")

# Note: The exact methods and parameters for Memory Bank API interaction would be specific to the Vertex AI SDK.
```

--------------------------------

### Quickstart for Agent Development Kit (ADK)

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/quickstart

This snippet provides a quickstart guide for using the Agent Development Kit (ADK) for agent development. It is a preview feature.

```Python
import vertexai
from vertexai.preview.generative_models import GenerativeModel, Part

def main():
    vertexai.init(project="your-project-id", location="your-region")
    model = GenerativeModel("gemini-1.0-pro-001")
    response = model.generate_content("Hello, world!")
    print(response.text)

if __name__ == "__main__":
    main()

```

--------------------------------

### Quickstart with Agent Development Kit for Memory Bank

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/quickstart

Quickstart guide for using the Agent Development Kit (ADK) to interact with the Memory Bank. This covers basic operations for managing memories.

```Python
from google.generativeai.agent import Agent

# Assume agent is initialized
agent = Agent(name="MemoryAgentADK")

# Interact with Memory Bank using ADK
# agent.memory_bank.save("user_context", {"key": "value"})
# memory = agent.memory_bank.load("user_context")

print("Quickstart with Agent Development Kit for Memory Bank.")
```

--------------------------------

### RAG Quickstart for Python

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/grounding/grounding-search-suggestions

This section provides a quickstart guide for using the RAG Engine with Python. It likely covers setting up the environment, initializing the RAG engine, and performing basic operations like data ingestion and querying.

```Python
import vertexai
from vertexai.preview.generative_models import GenerativeModel, Part

# TODO(developer): Update and un-comment the following lines
# project_id = "your-project-id"
# location = "us-central1"

# vertexai.init(project=project_id, location=location)

# model = GenerativeModel("gemini-1.0-pro")
# response = model.generate_content("What is the meaning of life?")
# print(response.text)
```

--------------------------------

### Looker MCP Inspector Quickstart

Source: https://googleapis.github.io/genai-toolbox/getting-started

A quickstart guide for using the Looker MCP Inspector. Details how to set up and utilize the inspector tool within the MCP environment for Looker.

```Markdown
## Quickstart (MCP with Looker)

This guide focuses on the Looker MCP Inspector, providing instructions on how to set it up and use it effectively within the Managed Cloud Platform.
```

--------------------------------

### RAG Quickstart for Python

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/quickstart

This snippet provides a quickstart guide for using the RAG Engine with Python. It likely covers setting up the environment, initializing the RAG engine, and performing basic retrieval-augmented generation tasks.

```Python
import vertexai
from vertexai.preview.generative_models import GenerativeModel, Part

# Initialize Vertex AI
vertexai.init(project="your-project-id", location="your-location")

# Load the RAG model (example)
model = GenerativeModel("gemini-pro")

# Example prompt with grounding context (replace with actual grounding)
response = model.generate_content("What is Vertex AI?")

print(response.text)
```

--------------------------------

### Getting Started with AlloyDB AI-NL Tool

Source: https://googleapis.github.io/genai-toolbox/resources/tools/

Guides users on getting started with the AlloyDB AI-NL tool. This sample focuses on natural language interactions with AlloyDB.

```Python
from genai_toolbox.samples.alloydb import ai_nl

# Example usage:
# ai_nl.run_example()
```

--------------------------------

### Install Vertex AI SDK for Python (LangChain)

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/quickstart

Installs the Vertex AI SDK for Python with LangChain support, including packages for agent engines. This command ensures the latest version is installed and upgrades if necessary.

```bash
pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,langchain]
```

--------------------------------

### Quickstart with BigQuery (Local)

Source: https://googleapis.github.io/genai-toolbox/how-to/export_telemetry/

Provides a quickstart guide for using BigQuery locally. This sample demonstrates basic BigQuery interactions.

```Python
from genai_toolbox.samples.bigquery import local_quickstart

# Example usage:
# local_quickstart.run_quickstart()
```

--------------------------------

### Getting Started with AlloyDB AI-NL Tool

Source: https://googleapis.github.io/genai-toolbox/how-to/export_telemetry/

Guides users on getting started with the AlloyDB AI-NL tool. This sample focuses on natural language interactions with AlloyDB.

```Python
from genai_toolbox.samples.alloydb import ai_nl

# Example usage:
# ai_nl.run_example()
```

--------------------------------

### Start ADK API Server (Shell)

Source: https://google.github.io/adk-docs/get-started/quickstart/

This command starts a local FastAPI server using `adk api_server`. It enables testing local cURL requests before deploying your agent, facilitating a streamlined development workflow.

```Shell
adk api_server
```

--------------------------------

### LiteLLM Development Setup and Commands

Source: https://github.com/BerriAI/litellm

Commands to clone the LiteLLM repository, install development dependencies, format code, run linters, and execute unit tests.

```bash
git clone https://github.com/BerriAI/litellm.git
cd litellm
make install-dev
make format
make lint
make test-unit
make format-check
```

--------------------------------

### Install FastMCP using uv

Source: https://github.com/jlowin/fastmcp

This command installs the FastMCP library using the uv package installer. It's the recommended way to get started with FastMCP.

```Shell
uv pip install fastmcp
```

--------------------------------

### Install Vertex AI SDK for Python (LlamaIndex)

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/quickstart

Installs the Vertex AI SDK for Python with LlamaIndex support, including packages for agent engines. This command ensures the latest version is installed and upgrades if necessary.

```bash
pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,llama_index]
```

--------------------------------

### Install Google ADK with pip

Source: https://adk-labs.github.io/adk-docs/ja/

This command installs the Google ADK package using pip, the Python package installer. It is the primary method for getting started with the ADK in a Python environment.

```python
pip install google-adk
```

--------------------------------

### Quickstart with Memory Bank API

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/overview

This quickstart guide demonstrates how to use the Memory Bank feature through API calls. It covers the basic steps for interacting with the Memory Bank to store and retrieve information.

```Python
# Conceptual Python code for Memory Bank quickstart via API
# from google.cloud import aiplatform

# aiplatform.init(project='your-project-id', location='us-central1')

# # Assume Memory Bank is configured and accessible
# # Example: Storing a memory
# memory_id = "user_123_session_abc"
# memory_content = {"last_interaction": "What is the capital of France?", "response": "Paris."}

# # aiplatform.memory_bank.store(id=memory_id, data=memory_content)
# print(f"Stored memory with ID: {memory_id}")

# # Example: Fetching a memory
# # retrieved_memory = aiplatform.memory_bank.get(id=memory_id)
# # print(f"Retrieved memory: {retrieved_memory}")

# Note: The exact methods and parameters for Memory Bank API interaction would be specific to the Vertex AI SDK.
```

--------------------------------

### Install Vertex AI SDK for Python (AG2)

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/quickstart

Installs the Vertex AI SDK for Python with AG2 support, including packages for agent engines. This command ensures the latest version is installed and upgrades if necessary.

```bash
pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,ag2]
```

--------------------------------

### Python Setup Script

Source: https://github.com/vllm-project/vllm

setup.py is the traditional script for building, distributing, and installing Python packages. For the vLLM project, it likely handles the packaging and installation process, including any C++ extensions.

```Python
setup.py
```

--------------------------------

### Install Vertex AI SDK for Python (LangGraph)

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/quickstart

Installs the Vertex AI SDK for Python with LangGraph support, including packages for agent engines. This command ensures the latest version is installed and upgrades if necessary.

```bash
pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,langchain]
```

--------------------------------

### Run Agent with Piped Input (Shell)

Source: https://google.github.io/adk-docs/get-started/quickstart/

Demonstrates how to run an agent and inject an initial prompt by piping text to the `adk run` command. This is useful for starting agent interactions with specific instructions.

```Shell
"Please start by listing files" | adk run file_listing_agent
```

--------------------------------

### AlloyDB AI NL Quickstart

Source: https://googleapis.github.io/genai-toolbox/getting-started

A quickstart guide for using the AI Natural Language tool with AlloyDB. Demonstrates how to integrate AI capabilities with AlloyDB data.

```Markdown
## Getting started with alloydb-ai-nl tool

This guide walks you through the initial setup and usage of the `alloydb-ai-nl` tool for natural language querying of your AlloyDB data.
```

--------------------------------

### RAG Quickstart for Python

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/overview

This section provides a quickstart guide for using the RAG Engine with Python. It likely covers setting up the environment, initializing the RAG engine, and performing basic operations like data ingestion and querying.

```Python
import vertexai
from vertexai.preview.generative_models import GenerativeModel, Part

# TODO(developer): Update and un-comment the following lines
# project_id = "your-project-id"
# location = "us-central1"

# vertexai.init(project=project_id, location=location)

# model = GenerativeModel("gemini-1.0-pro")
# response = model.generate_content("What is the meaning of life?")
# print(response.text)
```

--------------------------------

### Install Toolbox Go SDK

Source: https://github.com/googleapis/genai-toolbox

This snippet shows the command to install the Toolbox Go SDK using the `go get` command. It's a prerequisite for using the SDK in your Go projects.

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

--------------------------------

### Go Toolbox SDK Installation

Source: https://github.com/googleapis/genai-toolbox

This command installs the necessary Toolbox Go SDK, which is a prerequisite for using the Go-based LLM tool integration examples.

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

--------------------------------

### Install Toolbox Go SDK

Source: https://github.com/googleapis/genai-toolbox

This command installs the Google Cloud Toolbox Go SDK, which is a prerequisite for using the provided Go code examples.

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

--------------------------------

### Vector Search Overview and Quickstart

Source: https://cloud.google.com/vertex-ai/docs/pipelines/configure-project

Introduction to Vertex AI Vector Search, covering its overview, quickstart guide, and setup prerequisites. This helps users understand and begin using vector similarity searches.

```Python
# Example code for Vector Search quickstart would go here.
# This is a placeholder as the provided text only contains links.
print('Refer to Vertex AI documentation for specific code examples.')
```

--------------------------------

### Deploy a Redis cluster on GKE

Source: https://cloud.google.com/kubernetes-engine/docs/how-to/cluster-access-for-kubectl

This guide provides instructions for deploying a Redis cluster on GKE, focusing on configurations for a clustered Redis setup.

```yaml
# Example Kubernetes StatefulSet for Redis Cluster
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: redis-cluster
spec:
  serviceName: "redis-cluster-headless"
  replicas: 3
  selector:
    matchLabels:
      app: redis-cluster
  template:
    metadata:
      labels:
        app: redis-cluster
    spec:
      containers:
      - name: redis
        image: redis:latest
        command:
          - redis-server
          - /usr/local/etc/redis/redis.conf
        ports:
        - containerPort: 6379
          name: client
        - containerPort: 16379
          name: cluster
        volumeMounts:
        - name: redis-conf
          mountPath: /usr/local/etc/redis/
  volumeClaimTemplates:
  - metadata:
      name: redis-data
    spec:
      accessModes: [ "ReadWriteOnce" ]
      resources:
        requests:
          storage: 1Gi
---
# Example Kubernetes Headless Service for Redis Cluster
apiVersion: v1
kind: Service
metadata:
  name: redis-cluster-headless
spec:
  selector:
    app: redis-cluster
  ports:
  - port: 6379
    targetPort: 6379
    name: client
  - port: 16379
    targetPort: 16379
    name: cluster
  clusterIP: None
```

--------------------------------

### Quickstart for Agent Development Kit (ADK)

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/safety-system-instructions

This quickstart guide provides the essential steps to begin using the Agent Development Kit (ADK). It covers setting up your environment and creating your first agent.

```Python
# Conceptual Python code for ADK quickstart
# from google.cloud import aiplatform
# from vertexai.generative_models import GenerativeModel, Part

# aiplatform.init(project='your-project-id', location='us-central1')

# # Initialize a generative model
# model = GenerativeModel("gemini-1.0-pro")

# # Define a simple agent function (conceptual)
# def my_agent_function(user_input):
#     response = model.generate_content(f"User query: {user_input}")
#     return response.text

# # Example usage
# user_query = "What is the weather today?"
# agent_response = my_agent_function(user_query)
# print(f"Agent response: {agent_response}")

# Note: This is a high-level example. Actual ADK usage involves more complex configurations and interactions.
```

--------------------------------

### Install Vertex AI SDK for Python (ADK)

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/quickstart

Installs the Vertex AI SDK for Python with ADK support, including necessary packages for agent engines. This command ensures the latest version is installed and upgrades if necessary.

```bash
pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,adk]
```

--------------------------------

### Quickstart with Memory Bank ADK

Source: https://cloud.google.com/vertex-ai/generative-ai/docs/grounding/grounding-search-suggestions

This quickstart guide explains how to utilize the Memory Bank feature within the Agent Development Kit (ADK). It covers the initial steps for integrating memory capabilities into your agents.

```Python
# Conceptual Python code for Memory Bank quickstart with ADK
# from vertexai.generative_models import GenerativeModel, Part
# from vertexai.preview.generative_models import ToolConfig, FunctionDeclaration

# # Assume ADK and Memory Bank are configured
# model = GenerativeModel("gemini-1.0-pro")

# # Define a tool that interacts with Memory Bank (conceptual)
# @tool
# def get_user_preference(user_id: str) -> str:
#     """Retrieves user preferences from memory."""
#     # Logic to fetch from Memory Bank using user_id
#     return "User preference: Likes blue color."

# # Create an agent with the tool
# agent = create_tool_calling_agent(model, [get_user_preference], prompt)
# agent_executor = AgentExecutor(agent=agent, tools=[get_user_preference])

# # Use the agent
# response = agent_executor.invoke({"input": "What are my preferences?"})
# print(response['output'])

# Note: This example illustrates the concept. Actual ADK integration with Memory Bank would involve specific ADK constructs.
```

--------------------------------

### Load and Use Tool with OpenAI Go SDK

Source: https://googleapis.github.io/genai-toolbox/getting-started/introduction/

Demonstrates loading a tool using the Toolbox Go SDK and preparing it for integration with the OpenAI Go SDK. This involves client initialization and tool loading.

```Go
package main

import (
	"context"
	"encoding/json"
	"log"

	"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)
	if err != nil {
		log.Fatalf("Failed to create Toolbox client: %v", err)
	}

	// Framework agnostic tool
	tool, err := client.LoadTool("toolName", ctx)
	if err != nil {
		log.Fatalf("Failed to load tools: %v", err)
	}

```

--------------------------------

### Install Maven on Windows with Scoop

Source: https://maven.apache.org/install.html

Installs Maven on Windows using the Scoop package manager. Ensure Scoop is installed.

```powershell
scoop install main/maven
```

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