# How to integrate Gemini MCP with Google ADK

```json
{
  "title": "How to integrate Gemini MCP with Google ADK",
  "toolkit": "Gemini",
  "toolkit_slug": "gemini",
  "framework": "Google ADK",
  "framework_slug": "google-adk",
  "url": "https://composio.dev/toolkits/gemini/framework/google-adk",
  "markdown_url": "https://composio.dev/toolkits/gemini/framework/google-adk.md",
  "updated_at": "2026-05-12T10:12:37.656Z"
}
```

## Introduction

This guide walks you through connecting Gemini to Google ADK using the Composio tool router. By the end, you'll have a working Gemini agent that can summarize this research article in 100 words, generate a creative image of a futuristic city, create a 30-second video based on this script through natural language commands.
This guide will help you understand how to give your Google ADK agent real control over a Gemini account through Composio's Gemini MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Gemini with

- [ChatGPT](https://composio.dev/toolkits/gemini/framework/chatgpt)
- [OpenAI Agents SDK](https://composio.dev/toolkits/gemini/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/gemini/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/gemini/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/gemini/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/gemini/framework/codex)
- [Cursor](https://composio.dev/toolkits/gemini/framework/cursor)
- [VS Code](https://composio.dev/toolkits/gemini/framework/vscode)
- [OpenCode](https://composio.dev/toolkits/gemini/framework/opencode)
- [OpenClaw](https://composio.dev/toolkits/gemini/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/gemini/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/gemini/framework/cli)
- [LangChain](https://composio.dev/toolkits/gemini/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/gemini/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/gemini/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/gemini/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/gemini/framework/crew-ai)

## TL;DR

Here's what you'll learn:
- Get a Gemini account set up and connected to Composio
- Install the Google ADK and Composio packages
- Create a Composio Tool Router session for Gemini
- Build an agent that connects to Gemini through MCP
- Interact with Gemini using natural language

## What is Google ADK?

Google ADK (Agents Development Kit) is Google's framework for building AI agents powered by Gemini models. It provides tools for creating agents that can use external services through the Model Context Protocol.
Key features include:
- Gemini Integration: Native support for Google's Gemini models
- MCP Toolset: Built-in support for Model Context Protocol tools
- Streamable HTTP: Connect to external services through streamable HTTP
- CLI and Web UI: Run agents via command line or web interface

## What is the Gemini MCP server, and what's possible with it?

The Gemini MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Gemini account. It provides structured and secure access to Gemini's multimodal AI features, so your agent can generate text, images, and videos, analyze content, and manage model resources on your behalf.
- Text and content generation: Instruct your agent to create high-quality, customized text using Gemini's advanced generative models—great for brainstorming, drafting, or summarizing information.
- Creative image and video generation: Ask the agent to generate original images or high-quality videos from text prompts using Gemini 2.5 Flash and Veo models, with fine control over style and format.
- Embedding and semantic analysis: Let your agent transform any text into rich semantic embeddings for similarity search, clustering, or classification tasks.
- Model discovery and optimization: Have the agent list available Gemini and Veo models, check their capabilities, and select the best fit for your project or workflow.
- Efficient resource management: Enable the agent to track video generation operations, download final assets, and optimize prompt inputs by counting tokens—all without manual intervention.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `GEMINI_COUNT_TOKENS` | Count Tokens (Gemini) | Counts the number of tokens in text using Gemini tokenization. Useful for estimating costs, checking input limits, and optimizing prompts before making API calls. |
| `GEMINI_EMBED_CONTENT` | Embed Content (Gemini) | Generates text embeddings using Gemini embedding models. Converts text into numerical vectors for semantic search, similarity comparison, clustering, and classification tasks. |
| `GEMINI_GENERATE_CONTENT` | Generate Content (Gemini) | Generates text content or speech audio from prompts using Gemini models. Supports text generation models (Gemini Flash, Pro) and text-to-speech models with configurable parameters. Generated text is nested at results[i].response.data.text. Output may be wrapped in markdown fences (e.g., ```html...```) or preceded by explanatory prose; strip these before file writing or rendering. |
| `GEMINI_GENERATE_IMAGE` | Generate Image (Nano Banana) | Generates images from text prompts using Gemini models (Nano Banana). Supports models: 'gemini-2.5-flash-image' (GA stable, fast), 'gemini-3-pro-image-preview' (Nano Banana Pro - advanced with 4K resolution, thinking mode, up to 14 reference images), and 'gemini-2.0-flash-exp-image-generation' (2.0 Flash experimental). Returns one image per call; images are uploaded to S3. Parse response at data.image.s3url or the text-type entry in data.content — prefer the URL to avoid base64 blobs. Always validate s3url before treating call as successful; a 200 response may contain only text with no image. Store s3url immediately as URLs can expire. Output formats are raster only (JPG/PNG/WebP); request PNG for transparency. Concurrent usage may trigger HTTP 429/RESOURCE_EXHAUSTED — keep concurrency ≤3 and use exponential backoff (1s→2s→4s, ~5 retries). NOTE NEVER EVER TRUE SYNC_TO_WORKBENCH IN RUBE_MULTI_EXECUTE_TOOL |
| `GEMINI_GENERATE_VIDEOS` | Generate Videos (Veo) | Generates videos from text prompts using Google's Veo models. Returns an operation_name for tracking; pass it verbatim (no edits) to GEMINI_WAIT_FOR_VIDEO or GEMINI_GET_VIDEOS_OPERATION. Jobs take 30–180+ seconds; wait 10s before first poll, then poll every 10–30s (allow up to 12 min). Successful results include data.video_file.s3url — missing s3url means failure. If done=true but no video_file, check raiMediaFilteredReasons (safety block); revise prompt and regenerate. Text-only; cannot accept image inputs. Max ~3–5 concurrent jobs; 429 RESOURCE_EXHAUSTED requires exponential backoff. For retries, always start a fresh call — never reuse a failed operation_name. |
| `GEMINI_LIST_MODELS` | List Models (Gemini API) | Lists available Gemini and Veo models with their capabilities and limits. Useful for discovering supported models and their features before making generation requests. Before calling video generation tools, verify model availability here — preview Veo models (e.g., veo-3.0-generate-preview) may be unavailable or return missing video URIs; prefer stable models like veo-2.0-generate-001. |
| `GEMINI_WAIT_FOR_VIDEO` | Wait and Download Video (Veo) | Polls a Veo video generation operation until completion, then downloads and returns the video as a FileDownloadable. Generation takes 30–120+ seconds (up to ~10–12 min); long waits are normal, not failures. On completion, the URL is nested at data.video_file.s3url — validate it is non-empty before downstream use. A done=true response without a valid s3url indicates safety filter rejection (check raiMediaFilteredReasons) or quota exhaustion — adjust the prompt and regenerate. On timeout, use GEMINI_GET_VIDEOS_OPERATION with incremental backoff before starting a new job. Keep parallel jobs to 3–5 to avoid 429 RESOURCE_EXHAUSTED errors. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Gemini MCP server is an implementation of the Model Context Protocol that connects your AI agent to Gemini. It provides structured and secure access so your agent can perform Gemini operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

Before starting, make sure you have:
- A Google API key for Gemini models
- A Composio account and API key
- Python 3.9 or later installed
- Basic familiarity with Python

### 1. Getting API Keys for Google and Composio

Google API Key
- Go to [Google AI Studio](https://aistudio.google.com/app/apikey) and create an API key.
- Copy the key and keep it safe. You will put this in GOOGLE_API_KEY.
Composio API Key and User ID
- Log in to the [Composio dashboard](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Go to Settings → API Keys and copy your Composio API key. Use this for COMPOSIO_API_KEY.
- Decide on a stable user identifier to scope sessions, often your email or a user ID. Use this for COMPOSIO_USER_ID.

### 2. Install dependencies

Inside your virtual environment, install the required packages.
What's happening:
- google-adk is Google's Agents Development Kit
- composio connects your agent to Gemini via MCP
- python-dotenv loads environment variables
```bash
pip install google-adk composio python-dotenv
```

### 3. Set up ADK project

Set up a new Google ADK project.
What's happening:
- This creates an agent folder with a root agent file and .env file
```bash
adk create my_agent
```

### 4. Set environment variables

Save all your credentials in the .env file.
What's happening:
- GOOGLE_API_KEY authenticates with Google's Gemini models
- COMPOSIO_API_KEY authenticates with Composio
- COMPOSIO_USER_ID identifies the user for session management
```bash
GOOGLE_API_KEY=your-google-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_USER_ID=your-user-id-or-email
```

### 5. Import modules and validate environment

What's happening:
- os reads environment variables
- Composio is the main Composio SDK client
- GoogleProvider declares that you are using Google ADK as the agent runtime
- Agent is the Google ADK LLM agent class
- McpToolset lets the ADK agent call MCP tools over HTTP
```python
import os
import warnings

from composio import Composio
from dotenv import load_dotenv
from google.adk.agents.llm_agent import Agent
from google.adk.tools.mcp_tool.mcp_session_manager import StreamableHTTPConnectionParams
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset

load_dotenv()

warnings.filterwarnings("ignore", message=".*BaseAuthenticatedTool.*")

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")
```

### 6. Create Composio client and Tool Router session

What's happening:
- Authenticates to Composio with your API key
- Declares Google ADK as the provider
- Spins up a short-lived MCP endpoint for your user and selected toolkit
- Stores the MCP HTTP URL for the ADK MCP integration
```python
composio_client = Composio(api_key=COMPOSIO_API_KEY)

composio_session = composio_client.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["gemini"],
)

COMPOSIO_MCP_URL = composio_session.mcp.url,
print(f"Composio MCP URL: {COMPOSIO_MCP_URL}")
```

### 7. Set up the McpToolset and create the Agent

What's happening:
- Connects the ADK agent to the Composio MCP endpoint through McpToolset
- Uses Gemini as the model powering the agent
- Lists exact tool names in instruction to reduce misnamed tool calls
```python
composio_toolset = McpToolset(
    connection_params=StreamableHTTPConnectionParams(
        url=COMPOSIO_MCP_URL,
        headers={"x-api-key": COMPOSIO_API_KEY}
    )
)

root_agent = Agent(
    model="gemini-2.5-flash",
    name="composio_agent",
    description="An agent that uses Composio tools to perform actions.",
    instruction=(
        "You are a helpful assistant connected to Composio. "
        "You have the following tools available: "
        "COMPOSIO_SEARCH_TOOLS, COMPOSIO_MULTI_EXECUTE_TOOL, "
        "COMPOSIO_MANAGE_CONNECTIONS, COMPOSIO_REMOTE_BASH_TOOL, COMPOSIO_REMOTE_WORKBENCH. "
        "Use these tools to help users with Gemini operations."
    ),
    tools=[composio_toolset],
)

print("\nAgent setup complete. You can now run this agent directly ;)")
```

### 8. Run the agent

Execute the agent from the project root. The web command opens a web portal where you can chat with the agent.
What's happening:
- adk run runs the agent in CLI mode
- adk web . opens a web UI for interactive testing
```bash
# Run in CLI mode
adk run my_agent

# Or run in web UI mode
adk web
```

## Complete Code

```python
import os
import warnings

from composio import Composio
from composio_google import GoogleProvider
from dotenv import load_dotenv
from google.adk.agents.llm_agent import Agent
from google.adk.tools.mcp_tool.mcp_session_manager import StreamableHTTPConnectionParams
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset

load_dotenv()
warnings.filterwarnings("ignore", message=".*BaseAuthenticatedTool.*")

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

composio_client = Composio(api_key=COMPOSIO_API_KEY, provider=GoogleProvider())

composio_session = composio_client.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["gemini"],
)

COMPOSIO_MCP_URL = composio_session.mcp.url


composio_toolset = McpToolset(
    connection_params=StreamableHTTPConnectionParams(
        url=COMPOSIO_MCP_URL,
        headers={"x-api-key": COMPOSIO_API_KEY}
    )
)

root_agent = Agent(
    model="gemini-2.5-flash",
    name="composio_agent",
    description="An agent that uses Composio tools to perform actions.",
    instruction=(
        "You are a helpful assistant connected to Composio. "
        "You have the following tools available: "
        "COMPOSIO_SEARCH_TOOLS, COMPOSIO_MULTI_EXECUTE_TOOL, "
        "COMPOSIO_MANAGE_CONNECTIONS, COMPOSIO_REMOTE_BASH_TOOL, COMPOSIO_REMOTE_WORKBENCH. "
        "Use these tools to help users with Gemini operations."
    ),  
    tools=[composio_toolset],
)

print("\nAgent setup complete. You can now run this agent directly ;)")
```

## Conclusion

You've successfully integrated Gemini with the Google ADK through Composio's MCP Tool Router. Your agent can now interact with Gemini using natural language commands.
Key takeaways:
- The Tool Router approach dynamically routes requests to the appropriate Gemini tools
- Environment variables keep your credentials secure and separate from code
- Clear agent instructions reduce tool calling errors
- The ADK web UI provides an interactive interface for testing and development
You can extend this setup by adding more toolkits to the toolkits array in your session configuration.

## How to build Gemini MCP Agent with another framework

- [ChatGPT](https://composio.dev/toolkits/gemini/framework/chatgpt)
- [OpenAI Agents SDK](https://composio.dev/toolkits/gemini/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/gemini/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/gemini/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/gemini/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/gemini/framework/codex)
- [Cursor](https://composio.dev/toolkits/gemini/framework/cursor)
- [VS Code](https://composio.dev/toolkits/gemini/framework/vscode)
- [OpenCode](https://composio.dev/toolkits/gemini/framework/opencode)
- [OpenClaw](https://composio.dev/toolkits/gemini/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/gemini/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/gemini/framework/cli)
- [LangChain](https://composio.dev/toolkits/gemini/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/gemini/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/gemini/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/gemini/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/gemini/framework/crew-ai)

## Related Toolkits

- [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed.
- [Composio search](https://composio.dev/toolkits/composio_search) - Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service.
- [Perplexityai](https://composio.dev/toolkits/perplexityai) - Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows.
- [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow.
- [Ai ml api](https://composio.dev/toolkits/ai_ml_api) - Ai ml api is a suite of AI/ML models for natural language and image tasks. It provides fast, scalable access to advanced AI capabilities for your apps and workflows.
- [Aivoov](https://composio.dev/toolkits/aivoov) - Aivoov is an AI-powered text-to-speech platform offering 1,000+ voices in over 150 languages. Instantly turn written content into natural, human-like audio for any application.
- [All images ai](https://composio.dev/toolkits/all_images_ai) - All-Images.ai is an AI-powered image generation and management platform. It helps you create, search, and organize images effortlessly with advanced AI capabilities.
- [Anthropic administrator](https://composio.dev/toolkits/anthropic_administrator) - Anthropic administrator is an API for managing Anthropic organizational resources like members, workspaces, and API keys. It helps you automate admin tasks and streamline resource management across your Anthropic organization.
- [Api labz](https://composio.dev/toolkits/api_labz) - Api labz is a platform offering a suite of AI-driven APIs and workflow tools. It helps developers automate tasks and build smarter, more efficient applications.
- [Apipie ai](https://composio.dev/toolkits/apipie_ai) - Apipie ai is an AI model aggregator offering a single API for accessing top AI models from multiple providers. It helps developers build cost-efficient, latency-optimized AI solutions without juggling multiple integrations.
- [Astica ai](https://composio.dev/toolkits/astica_ai) - Astica ai provides APIs for computer vision, NLP, and voice synthesis. Integrate advanced AI features into your app with a single API key.
- [Bigml](https://composio.dev/toolkits/bigml) - BigML is a machine learning platform that lets you build, train, and deploy predictive models from your data. Its intuitive interface and robust API make machine learning accessible and efficient.
- [Botbaba](https://composio.dev/toolkits/botbaba) - Botbaba is a platform for building, managing, and deploying conversational AI chatbots across messaging channels. It streamlines chatbot automation, making it easier to integrate AI into customer interactions.
- [Botpress](https://composio.dev/toolkits/botpress) - Botpress is an open-source platform for building, deploying, and managing chatbots. It helps teams automate conversations and deliver rich, interactive messaging experiences.
- [Chatbotkit](https://composio.dev/toolkits/chatbotkit) - Chatbotkit is a platform for building and managing AI-powered chatbots using robust APIs and SDKs. It lets you easily add conversational AI to your apps for better user engagement.
- [Cody](https://composio.dev/toolkits/cody) - Cody is an AI assistant built for businesses, trained on your company's knowledge and data. It delivers instant answers and insights, tailored for your team.
- [Context7 MCP](https://composio.dev/toolkits/context7_mcp) - Context7 MCP delivers live, version-specific code docs and examples right from the source. It helps developers and AI agents instantly retrieve authoritative programming info—no more out-of-date docs.
- [Customgpt](https://composio.dev/toolkits/customgpt) - CustomGPT.ai lets you build and deploy chatbots tailored to your own data and business needs. Get precise and context-aware AI conversations without writing code.
- [Datarobot](https://composio.dev/toolkits/datarobot) - Datarobot is a machine learning platform that automates model development, deployment, and monitoring. It empowers organizations to quickly gain predictive insights from large datasets.
- [Deepgram](https://composio.dev/toolkits/deepgram) - Deepgram is an AI-powered speech recognition platform for accurate audio transcription and understanding. It enables fast, scalable speech-to-text with advanced audio intelligence features.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Gemini MCP?

With a standalone Gemini MCP server, the agents and LLMs can only access a fixed set of Gemini tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Gemini and many other apps based on the task at hand, all through a single MCP endpoint.

### Can I use Tool Router MCP with Google ADK?

Yes, you can. Google ADK fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Gemini tools.

### Can I manage the permissions and scopes for Gemini while using Tool Router?

Yes, absolutely. You can configure which Gemini scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

### How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Gemini data and credentials are handled as safely as possible.

---
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