# How to integrate Dreamstudio MCP with CrewAI

```json
{
  "title": "How to integrate Dreamstudio MCP with CrewAI",
  "toolkit": "Dreamstudio",
  "toolkit_slug": "dreamstudio",
  "framework": "CrewAI",
  "framework_slug": "crew-ai",
  "url": "https://composio.dev/toolkits/dreamstudio/framework/crew-ai",
  "markdown_url": "https://composio.dev/toolkits/dreamstudio/framework/crew-ai.md",
  "updated_at": "2026-05-12T10:09:45.548Z"
}
```

## Introduction

This guide walks you through connecting Dreamstudio to CrewAI using the Composio tool router. By the end, you'll have a working Dreamstudio agent that can transform your photo into a fantasy landscape, list all available dreamstudio image engines, show your dreamstudio credit balance through natural language commands.
This guide will help you understand how to give your CrewAI agent real control over a Dreamstudio account through Composio's Dreamstudio MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Dreamstudio with

- [OpenAI Agents SDK](https://composio.dev/toolkits/dreamstudio/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/dreamstudio/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/dreamstudio/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/dreamstudio/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/dreamstudio/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/dreamstudio/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/dreamstudio/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/dreamstudio/framework/cli)
- [Google ADK](https://composio.dev/toolkits/dreamstudio/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/dreamstudio/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/dreamstudio/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/dreamstudio/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/dreamstudio/framework/llama-index)

## TL;DR

Here's what you'll learn:
- Get a Composio API key and configure your Dreamstudio connection
- Set up CrewAI with an MCP enabled agent
- Create a Tool Router session or standalone MCP server for Dreamstudio
- Build a conversational loop where your agent can execute Dreamstudio operations

## What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.
Key features include:
- Agent Roles: Define specialized agents with specific goals and backstories
- Task Management: Create tasks with clear descriptions and expected outputs
- Crew Orchestration: Combine agents and tasks into collaborative workflows
- MCP Integration: Connect to external tools through Model Context Protocol

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

The Dreamstudio MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Dreamstudio account. It provides structured and secure access to your generative image tools, so your agent can generate images, edit visuals, check available engines, and monitor your account balance—all on your behalf.
- Image generation from prompts or reference: Ask your agent to create unique images based on detailed text prompts or by transforming existing images using Dreamstudio's powerful engines.
- Engine discovery and selection: Effortlessly retrieve a list of available Dreamstudio engines so your agent can choose the best model for your creative task.
- User account insights: Have your agent fetch your Dreamstudio account details, including user ID, email, and profile information, whenever you need them.
- Real-time credit and usage monitoring: Let your agent check your current credit balance and track usage to ensure you always have resources for your next creative project.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `DREAMSTUDIO_GENERATE_IMAGE_FROM_IMAGE` | Generate Image from Image | Tool to generate a new image from an initial image and text prompts. Use after you have a reference image and want to transform it via text guidance. |
| `DREAMSTUDIO_GENERATE_IMAGE_FROM_TEXT` | Generate Image from Text | Generate images from text prompts using Stability AI's text-to-image models. Use when you need to create images from textual descriptions without a reference image. Supports multiple prompts with weights, configurable dimensions, and various generation parameters. |
| `DREAMSTUDIO_LIST_ENGINES` | List Engines | List all available DreamStudio/Stability AI engines accessible with your API key. This action retrieves all AI models (engines) you can use for image generation and other tasks. Common engines include Stable Diffusion XL and various Stable Diffusion versions. Use this action to: - Discover which engines are available to your account - Get engine IDs needed for image generation requests - Check engine types and capabilities No parameters required - simply call to get the complete list of accessible engines. Using an unsupported engine_id in generation requests will cause failures; engines also vary in supported image sizes (typically capped near 1024x1024) and quality for specific tasks. |
| `DREAMSTUDIO_USER_ACCOUNT` | User Account | Retrieves authenticated user's account information from Stability AI. Returns user ID, email, organization memberships, and profile picture. No parameters required - uses authenticated session. |
| `DREAMSTUDIO_USER_BALANCE` | DreamStudio User Balance | Retrieves the user's current credit balance from their DreamStudio account. Use this tool to check how many credits are available before generating images or to monitor remaining credits after API operations. Credits are consumed when generating images through the Stability AI API. For large or high-resolution batch generation, verify sufficient balance first — insufficient credits will cause failures mid-run. |

## Supported Triggers

None listed.

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

The Dreamstudio MCP server is an implementation of the Model Context Protocol that connects your AI agent to Dreamstudio. It provides structured and secure access so your agent can perform Dreamstudio 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:
- Python 3.9 or higher
- A Composio account and API key
- A Dreamstudio connection authorized in Composio
- An OpenAI API key for the CrewAI LLM
- Basic familiarity with Python

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

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

**What's happening:**
- composio connects your agent to Dreamstudio via MCP
- crewai provides Agent, Task, Crew, and LLM primitives
- crewai-tools[mcp] includes MCP helpers
- python-dotenv loads environment variables from .env
```bash
pip install composio crewai crewai-tools[mcp] python-dotenv
```

### 3. Set up environment variables

Create a .env file in your project root.
What's happening:
- COMPOSIO_API_KEY authenticates with Composio
- USER_ID scopes the session to your account
- OPENAI_API_KEY lets CrewAI use your chosen OpenAI model
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here
```

### 4. Import dependencies

**What's happening:**
- CrewAI classes define agents and tasks, and run the workflow
- MCPServerHTTP connects the agent to an MCP endpoint
- Composio will give you a short lived Dreamstudio MCP URL
```python
import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

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

if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")
```

### 5. Create a Composio Tool Router session for Dreamstudio

**What's happening:**
- You create a Dreamstudio only session through Composio
- Composio returns an MCP HTTP URL that exposes Dreamstudio tools
```python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["dreamstudio"])

url = session.mcp.url
```

### 6. Initialize the MCP Server

**What's Happening:**
- Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
- MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
- Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
- Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
- Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.
```python
server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )
```

### 7. Create a CLI Chatloop and define the Crew

**What's Happening:**
- Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
- Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
- Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
- Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
- Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
- Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.
```python
print("Chat started! Type 'exit' or 'quit' to end.\n")

conversation_context = ""

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

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

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[agent], tasks=[task], verbose=False)
    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
```

## Complete Code

```python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

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.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["dreamstudio"],
)
url = session.mcp.url

# Configure LLM
llm = LLM(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY"),
)

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\n")

    conversation_context = ""

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

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

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")
```

## Conclusion

You now have a CrewAI agent connected to Dreamstudio through Composio's Tool Router. The agent can perform Dreamstudio operations through natural language commands.
Next steps:
- Add role-specific instructions to customize agent behavior
- Plug in more toolkits for multi-app workflows
- Chain tasks for complex multi-step operations

## How to build Dreamstudio MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/dreamstudio/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/dreamstudio/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/dreamstudio/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/dreamstudio/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/dreamstudio/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/dreamstudio/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/dreamstudio/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/dreamstudio/framework/cli)
- [Google ADK](https://composio.dev/toolkits/dreamstudio/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/dreamstudio/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/dreamstudio/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/dreamstudio/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/dreamstudio/framework/llama-index)

## Related Toolkits

- [Figma](https://composio.dev/toolkits/figma) - Figma is a collaborative interface design tool for teams and individuals. It streamlines design workflows with real-time collaboration and easy sharing.
- [Abyssale](https://composio.dev/toolkits/abyssale) - Abyssale is a creative automation platform for generating images, videos, GIFs, PDFs, and HTML5 content programmatically. It streamlines and scales visual content production for marketing, design, and operations teams.
- [Alttext ai](https://composio.dev/toolkits/alttext_ai) - AltText.ai is a service that generates alt text for images automatically. It helps boost accessibility and SEO for your visual content.
- [Bannerbear](https://composio.dev/toolkits/bannerbear) - Bannerbear is an API-driven platform for generating images and videos automatically at scale. It helps businesses create custom graphics, social visuals, and marketing assets using powerful templates.
- [Canva](https://composio.dev/toolkits/canva) - Canva is a drag-and-drop design suite for creating professional graphics, presentations, and marketing materials. It makes it easy for anyone to design with beautiful templates and a vast library of elements.
- [Claid ai](https://composio.dev/toolkits/claid_ai) - Claid.ai delivers AI-driven image editing APIs for tasks like background removal, upscaling, and color correction. It helps automate and enhance image workflows with powerful, developer-friendly tools.
- [Cloudinary](https://composio.dev/toolkits/cloudinary) - Cloudinary is a cloud-based platform for managing, uploading, and transforming images and videos. It streamlines media workflows and delivers optimized assets globally.
- [Cults](https://composio.dev/toolkits/cults) - Cults is a digital marketplace for 3D printing models, connecting designers and makers. It lets creators share, sell, and discover a huge variety of printable designs easily.
- [DeepImage](https://composio.dev/toolkits/deepimage) - DeepImage is an AI-powered image enhancer and upscaler. Get higher-quality images with just a few clicks.
- [Dynapictures](https://composio.dev/toolkits/dynapictures) - Dynapictures is a cloud-based platform for generating personalized images at scale. Instantly create hundreds of custom visuals using your data sources, like Google Sheets.
- [Fal.ai](https://composio.dev/toolkits/fal_ai) - Fal.ai is a generative media platform offering 600+ AI models for images, video, voice, and audio. Developers use Fal.ai for fast, scalable access to cutting-edge generative AI tools.
- [Gamma](https://composio.dev/toolkits/gamma) - Gamma is an AI-powered platform for making beautiful, interactive presentations and documents. It lets anyone create and share engaging decks with minimal effort.
- [Html to image](https://composio.dev/toolkits/html_to_image) - Html to image converts HTML and CSS into images or captures web page screenshots. Instantly generate visuals from code or web content—no manual screenshots needed.
- [Imagior](https://composio.dev/toolkits/imagior) - Imagior is an AI-powered image generation platform that lets you create and customize images using dynamic templates and APIs. Perfect for businesses and creators needing fast, scalable visuals without design hassle.
- [Imejis io](https://composio.dev/toolkits/imejis_io) - Imejis io is an API-based image generation platform with powerful customization and template support. It lets you create and modify images in seconds, no manual design work required.
- [Imgix](https://composio.dev/toolkits/imgix) - Imgix is a real-time image processing and delivery service for developers. It helps you optimize, transform, and deliver images efficiently at any scale.
- [Kraken io](https://composio.dev/toolkits/kraken_io) - Kraken.io is an image optimization and compression platform. It helps you shrink image file sizes while keeping visual quality intact.
- [Logo dev](https://composio.dev/toolkits/logo_dev) - Logo.dev is an API and database for high-resolution company logos and brand metadata. Instantly fetch official logos from any domain without scraping or manual searching.
- [Miro](https://composio.dev/toolkits/miro) - Miro is a collaborative online whiteboard platform for teams to brainstorm, design, and manage projects visually. It streamlines teamwork by enabling real-time idea sharing, diagramming, and workflow planning in a single space.
- [Mural](https://composio.dev/toolkits/mural) - Mural is a digital whiteboard platform for distributed visual collaboration. It helps teams brainstorm, map ideas, and diagram together in real time.

## Frequently Asked Questions

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

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

### Can I use Tool Router MCP with CrewAI?

Yes, you can. CrewAI 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 Dreamstudio tools.

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

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

---
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