How to integrate Google cloud vision MCP with LangChain

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Introduction

This guide walks you through connecting Google cloud vision to LangChain using the Composio tool router. By the end, you'll have a working Google cloud vision agent that can bulk import product images from gcs csv, list all vision ai service locations, create a new product for image recognition, delete an outdated product and its images through natural language commands.

This guide will help you understand how to give your LangChain 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.

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Google cloud vision project to Composio
  • Create a Tool Router MCP session for Google cloud vision
  • Initialize an MCP client and retrieve Google cloud vision tools
  • Build a LangChain agent that can interact with Google cloud vision
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

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.

Supported Tools & Triggers

Tools
Create Vision ProductTool to create and return a new Product resource.
Create ReferenceImageTool to create a ReferenceImage under a product.
Delete ProductTool to permanently delete a Product and its reference images.
Get ProductTool to get information associated with a Product.
Get Product SetTool to get a ProductSet.
Import Product SetsTool to asynchronously import reference images into ProductSets from a CSV in GCS.
List IndexEndpointsTool to list IndexEndpoints in a project and location.
List LocationsTool to list available Vision AI service locations for a project.
List Vision API OperationsTool to list operations that match the specified filter.
Purge ProductsTool to asynchronously delete products in a ProductSet or orphan products.
Update ProductTool to update a Product's mutable fields: displayName, description, and productLabels.
Update Product SetTool to update a ProductSet resource.
Add Product to ProductSetTool to add a Product to a specified ProductSet.
Cancel Vision OperationTool to cancel a long-running Vision API operation.
Delete Vision API OperationTool to delete a long-running Vision API operation.
Delete Product SetTool to permanently delete a ProductSet.
Delete Reference ImageTool to permanently delete a reference image.
Get Vision API OperationTool to get the latest state of a long-running operation.
Get Reference ImageTool to get information associated with a ReferenceImage.
List Products in ProductSetTool to list Products in a specified ProductSet.
List ProjectsTool to list Google Cloud projects accessible by the authenticated user.
List Reference ImagesTool to list reference images for a product.
Remove Product from ProductSetTool to remove a Product from a specified ProductSet.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

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

How the Tool Router works

The Tool Router 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

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming

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

pip install composio-langchain langchain-mcp-adapters langchain python-dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • composio-langchain provides Composio integration for LangChain
  • langchain-mcp-adapters enables MCP client connections
  • langchain is the core agent framework
  • python-dotenv loads environment variables

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models

Import dependencies

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Google cloud vision functionality through MCP

Initialize Composio client

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Google cloud vision tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Google cloud vision
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['google_cloud_vision']
)

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Google cloud vision tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
  • This approach allows the agent to dynamically load and use Google cloud vision tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "google_cloud_vision-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Google cloud vision MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Google cloud vision tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

conversation_history = []

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")

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

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

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

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

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['google_cloud_vision']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "google_cloud_vision-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    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")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

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

Conclusion

You've successfully built a LangChain agent that can interact with Google cloud vision through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

How to build Google cloud vision MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Google cloud vision MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Google cloud vision MCP with Claude Agent SDK

Google ADK

Google ADK

Use Google cloud vision MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Google cloud vision MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Google cloud vision MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Google cloud vision MCP with LlamaIndex

CrewAI

CrewAI

Use Google cloud vision MCP with CrewAI

FAQ

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

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.

Can I use Tool Router MCP with LangChain?

Yes, you can. LangChain 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.

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

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.

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 Google cloud vision data and credentials are handled as safely as possible.

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