How to integrate Moderation API MCP with CrewAI

This guide walks you through connecting Moderation API to CrewAI using the Composio tool router. By the end, you'll have a working Moderation API agent that can review flagged content, approve safe posts, block repeat offenders, and manage your wordlists through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Moderation API account through Composio's Moderation API MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Moderation API is a content safety service for detection, human review, author management, and moderation actions. Use it to keep user-generated content safer with clear workflows and audit-ready decisions.

11 Tools

Introduction

This guide walks you through connecting Moderation API to CrewAI using the Composio tool router. By the end, you'll have a working Moderation API agent that can review flagged content, approve safe posts, block repeat offenders, and manage your wordlists through natural language commands.

This guide will help you understand how to give your CrewAI agent real control over a Moderation API account through Composio's Moderation API MCP server.

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

Also integrate Moderation API with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndex

TL;DR

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

The Moderation API MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Moderation API account. It provides structured and secure access to your moderation workflows, review queues, actions, and wordlists, so your agent can evaluate content, submit items for review, inspect queues, resolve reviewed items, and apply moderation actions on your behalf.

  • Content policy evaluation: Have your agent evaluate content without storing it, then report policy findings and the recommended moderation action.
  • Content submission and review: Let the agent submit content for moderation so it can be retained and added to a review queue based on your project settings.
  • Review queue management: Direct your agent to check queue totals, list and filter moderation items, and mark reviewed items as resolved or pending.
  • Moderation action execution: Instruct your agent to view available moderation actions and apply the appropriate action to content or authors.
  • Wordlist management: Have your agent list wordlists, inspect their current words, and add or remove terms from an existing wordlist.

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 step08 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Moderation API connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python
2

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.
3

Install dependencies

bash
pip install composio crewai crewai-tools[mcp] python-dotenv
What's happening:
  • composio connects your agent to Moderation API via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools[mcp] includes MCP helpers
  • python-dotenv loads environment variables from .env
4

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
5

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 Moderation API MCP URL
6

Create a Composio Tool Router session for Moderation API

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

url = session.mcp.url
What's happening:
  • You create a Moderation API only session through Composio
  • Composio returns an MCP HTTP URL that exposes Moderation API tools
7

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.
8

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 Moderation API 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=["moderationapi"],
)
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 Moderation API through Composio's Tool Router. The agent can perform Moderation API 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
TOOLS

Supported Tools

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

Evaluate Content Without Storage

Evaluate content without retaining it or adding it to a review queue, and return policy findings and the recommended moderation action.

Execute Moderation Action

Apply a discovered moderation action to content items or authors.

Get Account and Quota

Return the connected Moderation API account, plan, current project, and current quota values.

Get Review Queue

Return metadata and item counts for a review queue by ID.

Get Wordlist

Return one wordlist and its current words by ID.

List Moderation Actions

List moderation actions available to the organization, optionally restricted to a review queue.

List Review Queue Items

List and filter one page of moderation items in a review queue.

List Wordlists

List the organization's wordlists and their identifiers.

Modify Wordlist Words

Add words to or remove words from an existing wordlist.

Set Review Queue Item Status

Mark a review queue item resolved or return it to pending.

Submit Content for Moderation

Evaluate content and allow Moderation API to retain it and add it to a review queue according to the project's configuration.

FRAMEWORKS

How to build Moderation API MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Moderation API MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Moderation API MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Moderation API MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Moderation API MCP with Claude Cowork

Codex logo

Codex

Use Moderation API MCP with Codex

OpenClaw logo

OpenClaw

Use Moderation API MCP with OpenClaw

Hermes logo

Hermes

Use Moderation API MCP with Hermes

CLI logo

CLI

Use Moderation API MCP with CLI

Google ADK logo

Google ADK

Use Moderation API MCP with Google ADK

LangChain logo

LangChain

Use Moderation API MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Moderation API MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Moderation API MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Moderation API MCP with LlamaIndex

FAQ

Frequently asked questions

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

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 Moderation API tools.

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

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