How to integrate Grist MCP with CrewAI

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Introduction

This guide walks you through connecting Grist to CrewAI using the Composio tool router. By the end, you'll have a working Grist agent that can add new sales data to q2 table, create a document for project planning, delete outdated rows from inventory sheet, remove email column from contacts table through natural language commands.

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

The Grist MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Grist account. It provides structured and secure access to your spreadsheets and documents, so your agent can perform actions like adding records, creating tables, managing documents, and handling attachments on your behalf.

  • Automated data entry and record management: Instruct your agent to add, update, or delete records in specific Grist tables, streamlining your workflows and reducing manual input.
  • Table and document creation: Let your agent create new tables or entire documents in your workspaces, helping you quickly set up and expand your data structures as your needs grow.
  • Attachment and file management: Ask your agent to remove unwanted attachments from Grist documents, keeping your files organized and storage efficient.
  • Custom webhook integration: Have your agent register or delete webhooks for documents, enabling real-time notifications and integrations with other tools or services you rely on.
  • User and access provisioning via SCIM: Direct your agent to create or delete SCIM users as needed, making it easy to manage who has access to your Grist environment.

Supported Tools & Triggers

Tools
Add RecordsTool to add records to a specified table in a Grist document.
Create DocumentTool to create a document in a workspace.
Create SCIM UserTool to create a new SCIM user.
Create TableTool to create tables in a document.
Create Document WebhookTool to create a new webhook for a specified document.
Delete AttachmentTool to delete a specified attachment from a Grist document.
Delete ColumnTool to delete a column from a Grist document table.
Delete Grist Table RecordsTool to delete records from a specified Grist table.
Delete SCIM UserTool to delete a specified user via SCIM.
Delete WebhookTool to delete a webhook from a Grist document.
Download All Attachments ArchiveTool to download all attachments from a Grist document as a .
Download AttachmentTool to download the binary data of an attachment.
Download Grist Attachment ContentTool to download the raw bytes of an attachment.
Fetch Column MetadataTool to fetch metadata for a specific column in a Grist document table.
Fetch Document MetadataTool to fetch metadata for a specified Grist document.
Fetch Table MetadataTool to retrieve metadata for a specified table in a Grist document.
Get Org AccessTool to fetch org access details.
Get UsersTool to retrieve a list of users via SCIM v2.
List AttachmentsTool to list all attachments in a Grist document.
List ColumnsTool to list all columns in a specified Grist table.
List OrganizationsTool to list all organizations accessible to the authenticated user.
List RecordsTool to retrieve records from a specified table within a Grist document.
List TablesTool to list all tables within a specified document.
List WebhooksTool to list webhooks configured for a document.
List WorkspacesTool to list all workspaces and documents accessible to the authenticated user on the current site.
Run SQL QueryTool to execute a read-only SQL SELECT query on a Grist document.
Update Column MetadataTool to update metadata for one or more columns in a Grist document table.
Update Document MetadataTool to update metadata for a specified Grist document.
Update RecordsTool to update records in a specified table within a document.
Update Table MetadataTool to update metadata for a specified table.
Update WebhookTool to update an existing webhook for a specified document.
Upload AttachmentTool to upload one or more attachments to a Grist 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 Grist 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 Grist 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 Grist MCP URL

Create a Composio Tool Router session for Grist

python
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
    user_id=os.getenv("USER_ID"),
    toolkits=["grist"],
)
url = session.mcp.url
What's happening:
  • You create a Grist only session through Composio
  • Composio returns an MCP HTTP URL that exposes Grist 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="Grist Assistant",
    goal="Help users interact with Grist through natural language commands",
    backstory=(
        "You are an expert assistant with access to Grist tools. "
        "You can perform various Grist 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 Grist 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 Grist 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 Grist 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_grist_agent.py

Complete Code

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

python
# file: crewai_grist_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 Grist session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["grist"],
    )
    url = session.mcp.url

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

    # Create Grist assistant agent
    toolkit_agent = Agent(
        role="Grist Assistant",
        goal="Help users interact with Grist through natural language commands",
        backstory=(
            "You are an expert assistant with access to Grist tools. "
            "You can perform various Grist 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 Grist 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 Grist 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 Grist through Composio's Tool Router. The agent can perform Grist 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 Grist MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Grist MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Grist MCP with Claude Agent SDK

Google ADK

Google ADK

Use Grist MCP with Google ADK

LangChain

LangChain

Use Grist MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Grist MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Grist MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Grist MCP with LlamaIndex

FAQ

What are the differences in Tool Router MCP and Grist MCP?

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

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

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

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