How to integrate Google Docs MCP with CrewAI

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Introduction

This guide walks you through connecting Google Docs to CrewAI using the Composio tool router. By the end, you'll have a working Google Docs agent that can create a new meeting notes document, copy last week's project summary template, add bullet points to the action items section, delete the footer from my current draft through natural language commands.

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

The Google Docs 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 Docs account. It provides structured and secure access to your documents, so your agent can create, copy, edit, and organize Google Docs on your behalf.

  • Automated document creation and duplication: Let your agent generate new Google Docs from scratch or copy existing documents to quickly use templates or preserve originals.
  • Rich content editing and formatting: Direct your agent to add headers, footers, footnotes, bullet lists, and more—making it easy to update and format documents programmatically.
  • Targeted content manipulation: Have your agent delete specific content ranges, paragraphs, or sections within any document to keep your files up to date.
  • Named range management: Empower your agent to create and manage named ranges for easier referencing, collaboration, and advanced document workflows.
  • Markdown-based document generation: Allow the agent to create new Google Docs directly from markdown content, streamlining content migration from other tools or sources.

Supported Tools & Triggers

Tools
Triggers
Copy Google DocumentTool to create a copy of an existing google document.
Create a documentCreates a new google docs document using the provided title as filename and inserts the initial text at the beginning if non-empty, returning the document's id and metadata (excluding body content).
Create Document MarkdownCreates a new google docs document, optionally initializing it with a title and content provided as markdown text.
Create FooterTool to create a new footer in a google document.
Create FootnoteTool to create a new footnote in a google document.
Create HeaderTool to create a new header in a google document.
Create Named RangeTool to create a new named range in a google document.
Create Paragraph BulletsTool to add bullets to paragraphs within a specified range in a google document.
Delete Content Range in DocumentTool to delete a range of content from a google document.
Delete FooterTool to delete a footer from a google document.
Delete HeaderDeletes the header from the specified section or the default header if no section is specified.
Delete Named RangeTool to delete a named range from a google document.
Delete Paragraph BulletsTool to remove bullets from paragraphs within a specified range in a google document.
Delete TableTool to delete an entire table from a google document.
Delete Table ColumnTool to delete a column from a table in a google document.
Delete Table RowTool to delete a row from a table in a google document.
Get Charts from SpreadsheetTool to retrieve a list of all charts from a specified google sheets spreadsheet.
Get document by idRetrieves an existing google document by its id; will error if the document is not found.
Insert Inline ImageTool to insert an image from a given uri at a specified location in a google document as an inline image.
Insert Page BreakTool to insert a page break into a google document.
Insert Table in Google DocTool to insert a table into a google document.
Insert Table ColumnTool to insert a new column into a table in a google document.
Insert Text into DocumentTool to insert a string of text at a specified location within a google document.
List Charts from SpreadsheetTool to retrieve a list of charts with their ids and metadata from a google sheets spreadsheet.
Replace All Text in DocumentTool to replace all occurrences of a specified text string with another text string throughout a google document.
Replace Image in DocumentTool to replace a specific image in a document with a new image from a uri.
Search DocumentsSearch for google documents using various filters including name, content, date ranges, and more.
Unmerge Table CellsTool to unmerge previously merged cells in a table.
Update Document MarkdownReplaces the entire content of an existing google docs document with new markdown text; requires edit permissions for the document.
Update Document StyleTool to update the overall document style, such as page size, margins, and default text direction.
Update existing documentApplies programmatic edits, such as text insertion, deletion, or formatting, to a specified google doc using the `batchupdate` api method.
Update Table Row StyleTool to update the style of a table row in a google document.

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 Google Docs 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 python-dotenv
What's happening:
  • composio connects your agent to Google Docs via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools 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
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional import if you plan to adapt tools
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()
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 Google Docs MCP URL

Create a Composio Tool Router session for Google Docs

python
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
    user_id=os.getenv("USER_ID"),
    toolkits=["googledocs"],
)
url = session.mcp.url
What's happening:
  • You create a Google Docs only session through Composio
  • Composio returns an MCP HTTP URL that exposes Google Docs tools

Configure the LLM

python
llm = LLM(
    model="gpt-5-mini",
    api_key=os.getenv("OPENAI_API_KEY"),
)
What's happening:
  • CrewAI will call this LLM for planning and responses
  • You can swap in a different model if needed

Attach the MCP server and create the agent

python
toolkit_agent = Agent(
    role="Google Docs Assistant",
    goal="Help users interact with Google Docs through natural language commands",
    backstory=(
        "You are an expert assistant with access to Google Docs tools. "
        "You can perform various Google Docs operations on behalf of the user."
    ),
    mcps=[
        MCPServerHTTP(
            url=url,
            streamable=True,
            cache_tools_list=True,
            headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
        ),
    ],
    llm=llm,
    verbose=True,
    max_iter=10,
)
What's happening:
  • MCPServerHTTP connects the agent to the Google Docs MCP endpoint
  • cache_tools_list saves a tools catalog for faster subsequent runs
  • verbose helps you see what the agent is doing

Add a REPL loop with Task and Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to perform Google Docs operations.\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"Based on the conversation history:\n{conversation_context}\n\n"
            f"Current user request: {user_input}\n\n"
            f"Please help the user with their Google Docs related request."
        ),
        expected_output="A helpful response addressing the user's request",
        agent=toolkit_agent,
    )

    crew = Crew(
        agents=[toolkit_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:
  • You build a simple chat loop and keep a running context
  • Each user turn becomes a Task handled by the same agent
  • Crew executes the task and returns a response

Run the application

python
if __name__ == "__main__":
    main()
What's happening:
  • Standard Python entry point so you can run python crewai_googledocs_agent.py

Complete Code

Here's the complete code to get you started with Google Docs and CrewAI:

python
# file: crewai_googledocs_agent.py
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()

def main():
    # Initialize Composio and create a Google Docs session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["googledocs"],
    )
    url = session.mcp.url

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

    # Create Google Docs assistant agent
    toolkit_agent = Agent(
        role="Google Docs Assistant",
        goal="Help users interact with Google Docs through natural language commands",
        backstory=(
            "You are an expert assistant with access to Google Docs tools. "
            "You can perform various Google Docs operations on behalf of the user."
        ),
        mcps=[
            MCPServerHTTP(
                url=url,
                streamable=True,
                cache_tools_list=True,
                headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
            ),
        ],
        llm=llm,
        verbose=True,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Try asking the agent to perform Google Docs operations.\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"Based on the conversation history:\n{conversation_context}\n\n"
                f"Current user request: {user_input}\n\n"
                f"Please help the user with their Google Docs related request."
            ),
            expected_output="A helpful response addressing the user's request",
            agent=toolkit_agent,
        )

        crew = Crew(
            agents=[toolkit_agent],
            tasks=[task],
            verbose=False,
        )

        result = crew.kickoff()
        response = str(result)

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

if __name__ == "__main__":
    main()

Conclusion

You now have a CrewAI agent connected to Google Docs through Composio's Tool Router. The agent can perform Google Docs 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 Google Docs MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Google Docs MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Google Docs MCP with Claude Agent SDK

Google ADK

Google ADK

Use Google Docs MCP with Google ADK

LangChain

LangChain

Use Google Docs MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Google Docs MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Google Docs MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Google Docs MCP with LlamaIndex

FAQ

What are the differences in Tool Router MCP and Google Docs MCP?

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

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

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

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