# How to integrate Replicate MCP with Google ADK

```json
{
  "title": "How to integrate Replicate MCP with Google ADK",
  "toolkit": "Replicate",
  "toolkit_slug": "replicate",
  "framework": "Google ADK",
  "framework_slug": "google-adk",
  "url": "https://composio.dev/toolkits/replicate/framework/google-adk",
  "markdown_url": "https://composio.dev/toolkits/replicate/framework/google-adk.md",
  "updated_at": "2026-05-12T10:23:52.496Z"
}
```

## Introduction

This guide walks you through connecting Replicate to Google ADK using the Composio tool router. By the end, you'll have a working Replicate agent that can run stable diffusion to generate an image, list all your uploaded files on replicate, get readme documentation for a model through natural language commands.
This guide will help you understand how to give your Google ADK agent real control over a Replicate account through Composio's Replicate MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Replicate with

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

## TL;DR

Here's what you'll learn:
- Get a Replicate account set up and connected to Composio
- Install the Google ADK and Composio packages
- Create a Composio Tool Router session for Replicate
- Build an agent that connects to Replicate through MCP
- Interact with Replicate 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 Replicate MCP server, and what's possible with it?

The Replicate MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Replicate account. It provides structured and secure access to your Replicate resources, so your agent can perform actions like running AI model predictions, managing files, browsing model collections, and retrieving model documentation on your behalf.
- Run and manage AI model predictions: Easily instruct your agent to create, monitor, and manage predictions on any deployed Replicate model using custom input parameters.
- Browse and discover model collections: Ask your agent to fetch and list available model collections or retrieve example predictions to explore what’s possible on Replicate.
- Upload and organize files: Let your agent upload new files, list all stored files, or inspect file details to streamline your model workflows.
- Access model metadata and documentation: Retrieve full model details, schemas, and markdown README docs for any model to help you choose and utilize the right model for your tasks.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `REPLICATE_ACCOUNT_GET` | Get Account Information | Tool to get authenticated account information. Use when you need to retrieve details about the account associated with the API token. |
| `REPLICATE_CANCEL_PREDICTION` | Cancel Prediction | Tool to cancel a prediction that is still running. Use when you need to stop an in-progress prediction to free up resources or halt execution. |
| `REPLICATE_COLLECTIONS_GET` | Get model collection | Tool to get a specific collection of models by its slug. Use when you need detailed information about a collection and its models. |
| `REPLICATE_COLLECTIONS_LIST` | List model collections | Tool to list all collections of models. Use when you need to retrieve available model collections. Collections are curated groupings of related models. Response includes only collection metadata (name, slug, description), not individual models within each collection; use REPLICATE_MODELS_GET for per-model details. Response may include a non-null `next` field indicating additional pages; follow it to enumerate all collections. |
| `REPLICATE_CREATE_MODEL` | Create Model | Tool to create a new Replicate model with specified owner, name, visibility, and hardware. Use when you need to create a destination model before launching LoRA/fine-tune training. |
| `REPLICATE_CREATE_PREDICTION` | Create Prediction | Tool to create a prediction for a Replicate Deployment. IMPORTANT: This action ONLY works with Replicate Deployments (persistent instances you create and manage), NOT public models. Deployments are created via REPLICATE_DEPLOYMENTS_CREATE. To run public models (e.g., 'meta/llama-2-70b-chat', 'stability-ai/sdxl'), use REPLICATE_MODELS_PREDICTIONS_CREATE instead. Use 'wait_for' to wait until the prediction completes. |
| `REPLICATE_DEPLOYMENTS_CREATE` | Create Deployment | Tool to create a new deployment with specified model, version, hardware, and scaling parameters. Use when you need to deploy a model for production use with auto-scaling. |
| `REPLICATE_DEPLOYMENTS_DELETE` | Delete Deployment | Tool to delete a deployment from your account. Use when you need to remove a deployment. Deployments must be offline and unused for at least 15 minutes before deletion. |
| `REPLICATE_DEPLOYMENTS_GET` | Get Deployment Details | Tool to get deployment details by owner and name. Use when you need information about a specific deployment including its release configuration and hardware settings. |
| `REPLICATE_DEPLOYMENTS_LIST` | List deployments | Tool to list all deployments associated with the account. Use when you need to retrieve deployment configurations and their latest releases. |
| `REPLICATE_CREATE_FILE` | Create File | Tool to create or upload a file to Replicate. Use when you need to upload file content with optional metadata. |
| `REPLICATE_FILES_DELETE` | Delete File | Tool to delete a file by its ID. Use when you need to remove a file from storage. Returns 204 No Content on success. |
| `REPLICATE_FILES_GET` | Get File Details | Tool to get details of a file by its ID. Use when you need to inspect uploaded file information before further operations. Returned URLs may be short-lived; download or persist needed files promptly after retrieval. |
| `REPLICATE_FILES_LIST` | List Files | Tool to retrieve a paginated list of uploaded files. Use to view all files created by the authenticated user or organization. Files are sorted with most recent first. Pagination is cursor-based: follow the next cursor until empty to retrieve all files. Limit requests to 1–2/second to avoid 429 Too Many Requests errors. Use to validate current file_ids before passing to prediction tools, as stale file_ids cause runtime errors. |
| `REPLICATE_GET_PREDICTION` | Get Prediction | Tool to get the status and output of a prediction by its ID. Use when you need to check on a running prediction or retrieve the results of a completed prediction. |
| `REPLICATE_HARDWARE_LIST` | List Available Hardware | Tool to list available hardware SKUs for models and deployments. Use when you need to see what hardware options are available on the Replicate platform. |
| `REPLICATE_MODELS_EXAMPLES_LIST` | List model examples | Tool to list example predictions for a specific model. Use when you want to retrieve author-provided illustrative examples after identifying the model. Returned examples are minimal working payloads; cross-reference with REPLICATE_MODELS_README_GET before calling REPLICATE_CREATE_PREDICTION to satisfy strict input validation. |
| `REPLICATE_MODELS_GET` | Get Model Details | Tool to get details of a specific model by owner and name. Consult the returned input schema before constructing any prediction request — each model defines its own required/optional fields (e.g., `prompt`, `aspect_ratio`, `version`); missing or unknown keys cause validation errors. Model schemas and available versions may change over time; recheck before production use. |
| `REPLICATE_MODELS_LIST` | List Public Models | Tool to list public models with pagination and sorting. Use when you need to browse available models or find models sorted by creation date. |
| `REPLICATE_MODELS_PREDICTIONS_CREATE` | Create Model Prediction | Tool to create a prediction using an official Replicate model. Use when you need to run inference with a specific model using its owner and name. Supports synchronous waiting (up to 60 seconds) and webhooks for async notifications. |
| `REPLICATE_MODELS_README_GET` | Get Model README | Tool to get the README content for a model in Markdown format. Consult alongside REPLICATE_MODELS_EXAMPLES_LIST before calling REPLICATE_CREATE_PREDICTION — Replicate enforces strict JSON schemas on model inputs and returns 422 errors for incorrect keys or types. Use after retrieving model details when you want to view its documentation. |
| `REPLICATE_MODELS_VERSIONS_GET` | Get Model Version | Tool to get a specific version of a model. Use when you need details about a particular model version including its schema and metadata. |
| `REPLICATE_MODELS_VERSIONS_LIST` | List Model Versions | Tool to list all versions of a specific model. Use when you need to see all available versions of a model, sorted by newest first. |
| `REPLICATE_CREATE_PREDICTION` | Create Prediction | Tool to create a prediction to run a model by version ID. Use when you have a specific model version identifier and need to run inference with provided inputs. Supports synchronous waiting and webhook notifications. |
| `REPLICATE_PREDICTIONS_LIST` | List All Predictions | Tool to list all predictions for the authenticated user or organization with pagination. Use when you need to retrieve prediction history or filter predictions by creation date. |
| `REPLICATE_SEARCH` | Search Models and Collections | Tool to search for models, collections, and docs using text queries (beta). Use when you need to find relevant models or collections based on keywords or descriptions. |
| `REPLICATE_TRAININGS_CANCEL` | Cancel Training | Tool to cancel an ongoing training operation in Replicate. Use when you need to stop a training job that is in progress. |
| `REPLICATE_TRAININGS_CREATE` | Create Training Job | Tool to create a training job for a specific model version. Use when you need to fine-tune a model with custom training data. Supports webhook notifications for training status updates. |
| `REPLICATE_TRAININGS_LIST` | List Training Jobs | Tool to list all training jobs for the authenticated user or organization with pagination. Use when you need to retrieve training history or check the status of training jobs. |
| `REPLICATE_UPDATE_MODELS` | Update Model Metadata | Tool to update metadata for a model including description, URLs, and README. Use when you need to modify a model's visibility, documentation, or associated links. |
| `REPLICATE_WEBHOOKS_SECRET_GET` | Get Webhook Signing Secret | Tool to get the signing secret for the default webhook. Use when you need to retrieve the secret key used to verify webhook authenticity. |

## Supported Triggers

None listed.

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

The Replicate MCP server is an implementation of the Model Context Protocol that connects your AI agent to Replicate. It provides structured and secure access so your agent can perform Replicate 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 Replicate 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=["replicate"],
)

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 Replicate 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=["replicate"],
)

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 Replicate operations."
    ),  
    tools=[composio_toolset],
)

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

## Conclusion

You've successfully integrated Replicate with the Google ADK through Composio's MCP Tool Router. Your agent can now interact with Replicate using natural language commands.
Key takeaways:
- The Tool Router approach dynamically routes requests to the appropriate Replicate 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 Replicate MCP Agent with another framework

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

With a standalone Replicate MCP server, the agents and LLMs can only access a fixed set of Replicate tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Replicate 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 Replicate tools.

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

Yes, absolutely. You can configure which Replicate 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 Replicate data and credentials are handled as safely as possible.

---
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