How to integrate Renderform MCP with Google ADK

This guide walks you through connecting Renderform to Google ADK using the Composio tool router. By the end, you'll have a working Renderform agent that can generate a personalized event invitation image, create a product promo banner with logo, make a qr code image for a website through natural language commands. This guide will help you understand how to give your Google ADK agent real control over a Renderform account through Composio's Renderform MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Renderform is an automation and design tool for creating images and templates with custom content. It makes personalizing graphics and managing creative assets fast and effortless.

7 Tools

Introduction

This guide walks you through connecting Renderform to Google ADK using the Composio tool router. By the end, you'll have a working Renderform agent that can generate a personalized event invitation image, create a product promo banner with logo, make a qr code image for a website through natural language commands.

This guide will help you understand how to give your Google ADK agent real control over a Renderform account through Composio's Renderform MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

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TL;DR

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

The Renderform MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Renderform account. It provides structured and secure access to your Renderform templates and image creation features, so your agent can generate customized images, update templates, add dynamic content, and automate design workflows on your behalf.

  • Dynamic image generation: Automatically create images based on templates with custom text, images, colors, or QR codes filled in by your agent.
  • Template management and population: Let your agent select and update design templates, swapping in new content or branding elements as needed.
  • Automated content personalization: Enable your agent to personalize graphics for marketing, events, or customer outreach by programmatically inserting unique details for each recipient.
  • Batch rendering and bulk design: Have your agent generate multiple images in one go for campaigns, product catalogs, or social posts, saving time and reducing manual work.
  • Seamless integration with other workflows: Connect image creation to broader automation flows, letting your agent trigger Renderform actions as part of end-to-end processes.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step09 STEPS
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
2

Getting API Keys for Google and Composio

Google API Key
  • Go to Google AI Studio 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.
  • 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.
3

Install dependencies

bash
pip install google-adk composio python-dotenv

Inside your virtual environment, install the required packages.

What's happening:

  • google-adk is Google's Agents Development Kit
  • composio connects your agent to Renderform via MCP
  • python-dotenv loads environment variables
4

Set up ADK project

bash
adk create my_agent

Set up a new Google ADK project.

What's happening:

  • This creates an agent folder with a root agent file and .env file
5

Set environment variables

bash
GOOGLE_API_KEY=your-google-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_USER_ID=your-user-id-or-email

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
6

Import modules and validate environment

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.")
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
7

Create Composio client and Tool Router session

python
composio_client = Composio(api_key=COMPOSIO_API_KEY)

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

COMPOSIO_MCP_URL = composio_session.mcp.url,
print(f"Composio MCP URL: {COMPOSIO_MCP_URL}")
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
8

Set up the McpToolset and create the Agent

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

print("\nAgent setup complete. You can now run this agent directly ;)")
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
9

Run the agent

bash
# Run in CLI mode
adk run my_agent

# Or run in web UI mode
adk web

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

Complete Code

Here's the complete code to get you started with Renderform and Google ADK:

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=["renderform"],
)

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

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

Conclusion

You've successfully integrated Renderform with the Google ADK through Composio's MCP Tool Router. Your agent can now interact with Renderform using natural language commands.

Key takeaways:

  • The Tool Router approach dynamically routes requests to the appropriate Renderform 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.

TOOLS

Supported Tools

Every Renderform action and event your agent gets out of the box.

Create PDF from URL

Tool to create a PDF from any website URL with customizable page format and auto-expiration.

Delete Result

Tool to delete a render result (image/PDF) by its identifier.

Get Result

Tool to retrieve detailed information about a specific render result by its identifier.

List Fonts

Tool to retrieve all available fonts including Google Fonts and custom uploaded fonts.

List Results (v2)

Tool to list all rendered images and PDFs with pagination support.

List Templates V2

Tool to get a list of all your templates with metadata and preview links.

Take Website Screenshot

Tool to capture a screenshot of a website with given dimensions.

FRAMEWORKS

How to build Renderform MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Renderform MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Renderform MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Renderform MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Renderform MCP with Claude Cowork

Codex logo

Codex

Use Renderform MCP with Codex

OpenClaw logo

OpenClaw

Use Renderform MCP with OpenClaw

Hermes logo

Hermes

Use Renderform MCP with Hermes

CLI logo

CLI

Use Renderform MCP with CLI

LangChain logo

LangChain

Use Renderform MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Renderform MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Renderform MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Renderform MCP with LlamaIndex

CrewAI logo

CrewAI

Use Renderform MCP with CrewAI

FAQ

Frequently asked questions

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

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 Renderform tools.

Yes, absolutely. You can configure which Renderform 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.

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 Renderform data and credentials are handled as safely as possible.

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