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How to integrate Moz MCP with CrewAI

Connect CrewAI to Moz MCP. Find top keywords for competitor site, audit your website for seo issues, and more using natural language, with authentication handled for you.

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Api Key

Moz is an SEO software suite offering keyword research, site audits, and rank tracking. It helps you boost organic search visibility and understand your competition.

12 Tools

Introduction

This guide walks you through connecting Moz to CrewAI using the Composio tool router. By the end, you'll have a working Moz agent that can fetch site metrics, check link status, find top pages and domains, and track your API usage through natural language commands.

This guide will help you understand how to give your CrewAI agent real control over a Moz account through Composio's Moz 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 Moz with

Claude CodeClaude CoworkOpenAI Agents SDKClaude Agent SDKGrok BotCodexOpenClawHermesAtomic AgentGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexPydantic AIAutoGen

TL;DR

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

The Moz MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Moz account. It provides structured and secure access to your Moz SEO suite, so your agent can perform actions like running keyword research, auditing sites, tracking keyword rankings, and analyzing competitors on your behalf.

  • Keyword research and suggestions: Instantly have your agent uncover high-potential keywords, analyze search volumes, and recommend keyword opportunities for your site or content strategy.
  • Comprehensive site audits: Let your agent scan your website for technical SEO issues, reporting on errors, warnings, and actionable improvements to boost search visibility.
  • Rank tracking and performance monitoring: Ask your agent to monitor keyword rankings over time, highlight position changes, and spot opportunities or threats in your SEO landscape.
  • Competitor domain analysis: Empower your agent to evaluate competitor sites, compare backlink profiles, and uncover gaps or strengths for strategic planning.
  • Backlink and authority insights: Retrieve detailed link metrics, domain authority scores, and identify valuable backlink opportunities or potentially harmful links.

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 Moz 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 Moz 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 Moz MCP URL
6

Create a Composio Tool Router session for Moz

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

url = session.mcp.url
What's happening:
  • You create a Moz only session through Composio
  • Composio returns an MCP HTTP URL that exposes Moz 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 Moz 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=["moz"],
)
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 Moz through Composio's Tool Router. The agent can perform Moz 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 Moz action and event your agent gets out of the box.

Fetch Metadata Index

Tool to fetch current index metadata from Moz via JSON-RPC.

Fetch Site Metrics

Fetch Moz site metrics for a single domain or URL, including available Domain Authority, Page Authority, Spam Score and link counts.

Get Global Top Root Domains

Fetch global top root domains from Moz, sorted by Domain Authority, with available authority, spam and linking-domain metrics.

Get API Usage Data

Tool to get API usage data including the number of rows consumed.

Get Global Top Pages

Fetch a paginated list of highest authority pages globally from Moz.

Get Index Metadata

Tool to fetch link index metadata from Moz.

Check Link Status

Tool to check if source URLs link to a target URL.

Get Global Top Domains

Fetch top ranking domains globally by Domain Authority from Moz.

List Global Top Pages (JSON-RPC)

Tool to fetch global top ranking pages from Moz using JSON-RPC API.

Lookup Quota Information

Tool to lookup API quota information including remaining rows, quota limits, and usage across different quota types.

Get Top Pages

Fetch top pages for a target domain or subdomain from Moz, with sorting by Page Authority or other metrics.

Get Usage Data

Look up Moz API usage and quota information to help monitor account usage and rate limits.

FRAMEWORKS

How to build Moz MCP Agent with another framework

Claude Code logo

Claude Code

Use Moz MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Moz MCP with Claude Cowork

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Moz MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Moz MCP with Claude Agent SDK

Grok Bot logo

Grok Bot

Use Moz MCP with Grok Bot

Codex logo

Codex

Use Moz MCP with Codex

OpenClaw logo

OpenClaw

Use Moz MCP with OpenClaw

Hermes logo

Hermes

Use Moz MCP with Hermes

Atomic Agent logo

Atomic Agent

Use Moz MCP with Atomic Agent

Google ADK logo

Google ADK

Use Moz MCP with Google ADK

LangChain logo

LangChain

Use Moz MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Moz MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Moz MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Moz MCP with LlamaIndex

Pydantic AI logo

Pydantic AI

Use Moz MCP with Pydantic AI

AutoGen logo

AutoGen

Use Moz MCP with AutoGen

FAQ

Frequently asked questions

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