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How to integrate Google cloud vision MCP with Autogen

Connect Autogen to Google cloud vision MCP. Bulk import product images from gcs csv, list all vision ai service locations, and more using natural language, with authentication handled for you.

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Google cloud vision

Api Key

Google Cloud Vision API adds advanced image analysis—like labeling, OCR, and detection—to apps. It helps you extract structured data and insights from images at scale.

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Introduction

This guide walks you through connecting Google cloud vision to AutoGen using the Composio tool router. By the end, you'll have a working Google cloud vision agent that can label and annotate images, extract text with OCR, run batch file analysis, manage product sets, and organize reference images through natural language commands.

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

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

For exact tool slugs and parameter tables, see the tool catalog in the Composio docs.

Also integrate Google cloud vision with

TL;DR

Here's what you'll learn:

Get and set up your OpenAI and Composio API keys
Install the required dependencies for Autogen and Composio
Initialize Composio and create a Tool Router session for Google cloud vision
Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
Configure an Autogen AssistantAgent that can call Google cloud vision tools
Run a live chat loop where you ask the agent to perform Google cloud vision operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

Multi-Agent Systems: Build collaborative agent workflows
MCP Workbench: Native support for Model Context Protocol tools
Streaming HTTP: Connect to external services through streamable HTTP
AssistantAgent: Pre-built agent class for tool-using assistants

What is the Google cloud vision MCP server, and what's possible with it?

The Google cloud vision MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Google Cloud Vision account. It provides structured and secure access to your image analysis resources, so your agent can perform actions like registering products, managing reference images, listing endpoints, and automating large-scale image operations on your behalf.

Product and reference image management: Easily create new products and add reference images for visual search, enabling your agent to organize and expand your vision datasets effortlessly.
Bulk import and product set operations: Let your agent import large numbers of reference images into product sets from Cloud Storage CSV files, streamlining dataset curation at scale.
Automated product cleanup and deletion: Direct your agent to purge unused or orphan products from your project, keeping your cloud resources tidy without manual effort.
Location and endpoint discovery: Quickly list available Vision AI service locations and existing IndexEndpoints, making it easy for your agent to select optimal regions and manage deployment targets.
Vision API operation tracking: Retrieve and review ongoing or past Vision API operations, so your agent can monitor processing jobs and ensure workflow transparency.

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:

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

Step-by-step Guide

Step by step08 STEPS

Prerequisites

You will need:

A Composio API key
An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
A Google cloud vision account you can connect to Composio
Some basic familiarity with Autogen and Python async

Getting API Keys for OpenAI and Composio

OpenAI API Key

Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
Keep the API key safe.

Composio API Key

Log in to the Composio dashboard.
Navigate to your API settings and generate a new API key.
Store this key securely as you'll need it for authentication.

Install dependencies

bash

pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

composio connects your agent to Google cloud vision via MCP
autogen-agentchat provides the AssistantAgent class
autogen-ext-openai provides the OpenAI model client
autogen-ext-tools provides MCP workbench support

Set up environment variables

bash

COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

COMPOSIO_API_KEY is required to talk to Composio
OPENAI_API_KEY is used by Autogen's OpenAI client
USER_ID is how Composio identifies which user's Google cloud vision connections to use

Import dependencies and create Tool Router session

python

import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Google cloud vision session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["google_cloud_vision"]
    )
    url = session.mcp.url

What's happening:

load_dotenv() reads your .env file
Composio(api_key=...) initializes the SDK
create(...) creates a Tool Router session that exposes Google cloud vision tools
session.mcp.url is the MCP endpoint that Autogen will connect to

Configure MCP parameters for Autogen

python

# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

url points to the Tool Router MCP endpoint from Composio
timeout is the HTTP timeout for requests
sse_read_timeout controls how long to wait when streaming responses
terminate_on_close=True cleans up the MCP server process when the workbench is closed

Create the model client and agent

python

# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Google cloud vision assistant agent with MCP tools
    agent = AssistantAgent(
        name="google_cloud_vision_assistant",
        description="An AI assistant that helps with Google cloud vision operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

OpenAIChatCompletionClient wraps the OpenAI model for Autogen
McpWorkbench connects the agent to the MCP tools
AssistantAgent is configured with the Google cloud vision tools from the workbench

Run the interactive chat loop

python

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Google cloud vision related question or task to the agent.\n")

# Conversation loop
while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

What's happening:

The script prompts you in a loop with You:
Autogen passes your input to the model, which decides which Google cloud vision tools to call via MCP
agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Google cloud vision and AutoGen:

python

import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Google cloud vision session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["google_cloud_vision"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Google cloud vision assistant agent with MCP tools
        agent = AssistantAgent(
            name="google_cloud_vision_assistant",
            description="An AI assistant that helps with Google cloud vision operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
        print("Ask any Google cloud vision related question or task to the agent.\n")

        # Conversation loop
        while True:
            user_input = input("You: ").strip()

            if user_input.lower() in ['exit', 'quit', 'bye']:
                print("\nGoodbye!")
                break

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You now have an Autogen assistant wired into Google cloud vision through Composio's Tool Router and MCP. From here you can:

Add more toolkits to the toolkits list, for example notion or hubspot
Refine the agent description to point it at specific workflows
Wrap this script behind a UI, Slack bot, or internal tool

Once the pattern is clear for Google cloud vision, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

TOOLS

Supported Tools

Every Google cloud vision action and event your agent gets out of the box.

Annotate Files with Vision API

Tool to perform image detection and annotation for batch files in Google Cloud Vision.

Async Batch Annotate Files

Tool to run asynchronous image detection and annotation for a list of generic files (PDF, TIFF, GIF).

Annotate Images

Run image detection and annotation for a batch of images using Google Cloud Vision API.

Annotate Images Async Batch

Tool to run asynchronous image detection and annotation for a batch of images.

Annotate Location Images

Annotate a batch of images for a specific project and location using Google Cloud Vision.

Create Vision Product

Creates a new Product resource in Google Cloud Vision Product Search.

Create Product Set

Creates a new ProductSet resource in Google Cloud Vision Product Search.

Create ReferenceImage

Tool to create a ReferenceImage under a product.

Delete Product

Deletes a product and its associated reference images from Google Cloud Vision Product Search using the product resource name.

Get Product

Tool to get information associated with a Product.

Get Product Set

Retrieves Cloud Vision ProductSet metadata by its full resource name, including its display name and indexing information.

Import Product Sets

Asynchronously imports product sets and reference images from a CSV file stored in Google Cloud Storage.

List Vision AI IndexEndpoints

Lists Vision AI IndexEndpoints in a specified project and location, helping discover endpoints for visual search and retrieval.

List Locations

Lists a fixed set of four Cloud Vision Product Search locations under a supplied project name, for use when choosing a region.

List Vision API Operations

List a page of Vision API operations under a project and location, optionally filtered.

Purge Products

Tool to asynchronously delete products in a ProductSet or orphan products.

Update Product

Tool to update a Product's mutable fields: displayName, description, and productLabels.

Update Product Set

Updates the display name of an existing Google Cloud Vision ProductSet identified by its resource name.

Add Product to ProductSet

Add a Product to a ProductSet in Google Cloud Vision Product Search.

Cancel Vision Operation

Starts asynchronous cancellation of a long-running Vision API operation.

Delete Vision API Operation

Deletes a long-running Google Cloud Vision API operation by resource name.

Delete Product Set

Deletes a Google Cloud Vision ProductSet by resource name.

Delete Reference Image

Permanently removes a reference image from a product in Google Cloud Vision Product Search.

Get Vision API Operation

Retrieves the latest state of a long-running Vision API operation by its resource name.

Get Reference Image

Tool to get information associated with a ReferenceImage.

List Products in ProductSet

Lists products associated with a specified ProductSet, with optional pagination.

List Projects

List Google Cloud projects accessible to the authenticated user through Cloud Resource Manager, with filtering and pagination.

List Reference Images

Tool to list reference images for a product.

Remove Product from ProductSet

Removes a Product from a specified ProductSet in Google Cloud Vision API.

FRAMEWORKS

How to build Google cloud vision MCP Agent with another framework

Claude Code

Use Google cloud vision MCP with Claude Code

Cursor

Use Google cloud vision MCP with Cursor

ChatGPT

Use Google cloud vision MCP with ChatGPT

Claude Cowork

Use Google cloud vision MCP with Claude Cowork

Antigravity

Use Google cloud vision MCP with Antigravity

OpenAI Agents SDK

Use Google cloud vision MCP with OpenAI Agents SDK

Claude Agent SDK

Use Google cloud vision MCP with Claude Agent SDK

Codex

Use Google cloud vision MCP with Codex

Kimi Code

Use Google cloud vision MCP with Kimi Code

Grok Build

Use Google cloud vision MCP with Grok Build

VS Code

Use Google cloud vision MCP with VS Code

OpenCode

Use Google cloud vision MCP with OpenCode

OpenClaw

Use Google cloud vision MCP with OpenClaw

Hermes

Use Google cloud vision MCP with Hermes

Atomic Agent

Use Google cloud vision MCP with Atomic Agent

CLI

Use Google cloud vision MCP with CLI

Google ADK

Use Google cloud vision MCP with Google ADK

LangChain

Use Google cloud vision MCP with LangChain

Vercel AI SDK

Use Google cloud vision MCP with Vercel AI SDK

Mastra AI

Use Google cloud vision MCP with Mastra AI

LlamaIndex

Use Google cloud vision MCP with LlamaIndex

CrewAI

Use Google cloud vision MCP with CrewAI

Pydantic AI

Use Google cloud vision MCP with Pydantic AI

MORE TOOLKITS

Explore Other Toolkits

Toolkit marketplace

Composio

No Auth

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

No Auth

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

Api Key

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

No Auth

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.

FAQ

Frequently asked questions

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

Yes, you can. Autogen 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 Google cloud vision tools.

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

Start with Google cloud vision.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Google cloud vision tool your agent needs.Free to start.

Start building

How to integrate Google cloud vision MCP with Autogen

Connect Google cloud vision without auth hassles

Introduction

Also integrate Google cloud vision with

TL;DR

What is AutoGen?

What is the Google cloud vision MCP server, and what's possible with it?

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

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import dependencies and create Tool Router session

Configure MCP parameters for Autogen

Create the model client and agent

Run the interactive chat loop

Complete Code

Conclusion

Supported Tools

How to build Google cloud vision MCP Agent with another framework

Claude Code

Cursor

ChatGPT

Claude Cowork

Antigravity

OpenAI Agents SDK

Claude Agent SDK

Codex

Kimi Code

Grok Build

VS Code

OpenCode

OpenClaw

Hermes

Atomic Agent

CLI

Google ADK

LangChain

Vercel AI SDK

Mastra AI

LlamaIndex

CrewAI

Pydantic AI

Explore Other Toolkits

Composio

Composio search

Perplexityai

Browser tool

Frequently asked questions

What are the differences in Tool Router MCP and Google cloud vision MCP?

Can I use Tool Router MCP with Autogen?

Can I manage the permissions and scopes for Google cloud vision while using Tool Router?

How safe is my data with Composio Tool Router?

Start with Google cloud vision.It takes 30 seconds.