How to integrate Scale ai MCP with CrewAI

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Introduction

This guide walks you through connecting Scale ai to CrewAI using the Composio tool router. By the end, you'll have a working Scale ai agent that can create image labeling task for dataset 'road-signs', list completed annotation tasks for project, fetch results of data labeling job through natural language commands.

This guide will help you understand how to give your CrewAI agent real control over a Scale ai account through Composio's Scale ai 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 a Composio API key and configure your Scale ai connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Scale ai
  • Build a conversational loop where your agent can execute Scale ai 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 Scale ai MCP server, and what's possible with it?

The Scale ai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Scale ai account. It provides structured and secure access so your agent can perform Scale ai operations on your behalf.

Supported Tools & Triggers

Tools
Add Studio AssignmentsTool to add project assignments to team members in Scale AI Studio.
Add Task TagsTool to add tags to an existing task.
Create BatchTool to create a new batch within a project.
Create Document Transcription TaskTool to create a document transcription task where workers transcribe and annotate information from single or multi-page documents.
Create Image Annotation TaskTool to create an image annotation task where annotators label images with vector geometric shapes (box, polygon, line, point, cuboid, ellipse).
Create Lidar Annotation TaskTool to create a lidar annotation task where annotators mark objects with 3D cuboids in 3D space.
Create LiDAR Segmentation TaskTool to create a LiDAR segmentation task where annotators assign semantic class labels to individual LiDAR points.
Create Named Entity Recognition TaskTool to create a named entity recognition task for labelers to highlight text entity mentions.
Create Segmentation Annotation TaskTool to create a segmentation task where annotators classify pixels in an image according to provided labels.
Create Text Collection TaskTool to create a textcollection task for collecting information from attachments and/or web sources.
Create Video Annotation TaskTool to create a video annotation task where annotators draw geometric shapes around specified objects across video frames.
Create Video Playback Annotation TaskTool to create a video playback annotation task where annotators draw shapes around specified objects in video files.
Delete Task TagsTool to remove specified tags from a Scale AI task.
Delete Task Unique IDTool to remove the unique identifier from a task.
Finalize BatchTool to finalize a batch so its tasks can be worked on.
Get AssetsTool to retrieve file assets with filtering capabilities by project and metadata.
Get BatchTool to retrieve the details of a batch with the specified name.
Get Batch StatusTool to retrieve the current status of a batch and task completion counts.
Get Fixless AuditsTool to retrieve fixless audits by task ID or audit ID.
Get ProjectTool to retrieve details about a specific Scale AI project using its unique identifier.
Get Quality LabelersTool to retrieve training attempts matching provided filter parameters.
Get Studio AssignmentsTool to retrieve current project assignments of all active team users in Scale AI Studio.
Get Studio BatchesTool to retrieve basic information about all pending batches in Studio.
Get TaskTool to retrieve detailed information about a specific task in Scale AI.
Get TeamsTool to retrieve basic information about all team members associated with the account.
Get Task by IDTool to retrieve detailed information about a specific task using its task ID.
Get Secure Task Response URLTool to retrieve secure authenticated task response data.
Import FileTool to import files from an external URL endpoint into Scale's system rather than uploading directly from local storage.
Invite Team MemberTool to invite users by email to team with specified role.
List BatchesTool to retrieve all batches in descending order by creation date.
List ProjectsTool to retrieve information for all projects in the Scale AI account with optional archived filtering.
List TasksTool to retrieve a paginated list of tasks in descending order by creation time.
Re-send Task CallbackTool to re-send a callback for a completed or errored task to the callback_url.
Remove Studio AssignmentsTool to unassign projects from specified team members in Scale AI Studio.
Reset Batch PrioritiesTool to restore batch priority order to default order (calibration batches first, then sorted by creation date).
Set Batch PrioritiesTool to modify batch priority order in Scale AI Studio.
Set Project OntologyTool to set ontologies on a Scale AI project.
Set Project ParametersTool to set default parameters for tasks created under a project.
Set Task MetadataTool to set key-value metadata on an existing Scale AI task.
Update Task Unique IDTool to update or assign a unique identifier to a task.
Upload FileTool to upload a local file to Scale's servers with a maximum size limit of 80 MB per file.

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, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Scale ai connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python

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

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_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 with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model

Import dependencies

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")
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 Scale ai MCP URL

Create a Composio Tool Router session for Scale ai

python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["scale_ai"])

url = session.mcp.url
What's happening:
  • You create a Scale ai only session through Composio
  • Composio returns an MCP HTTP URL that exposes Scale ai tools

Initialize the MCP Server

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,
    )
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.

Create a CLI Chatloop and define the Crew

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")
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.

Complete Code

Here's the complete code to get you started with Scale ai and CrewAI:

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=["scale_ai"],
)
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 Scale ai through Composio's Tool Router. The agent can perform Scale ai 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 Scale ai MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

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Claude Agent SDK

Claude Agent SDK

Use Scale ai MCP with Claude Agent SDK

Google ADK

Google ADK

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LangChain

LangChain

Use Scale ai MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Scale ai MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Scale ai MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Scale ai MCP with LlamaIndex

Claude Code

Claude Code

Use Scale ai MCP with Claude Code

Codex

Codex

Use Scale ai MCP with Codex

FAQ

What are the differences in Tool Router MCP and Scale ai MCP?

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

Can I manage the permissions and scopes for Scale ai while using Tool Router?

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

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