How to integrate Gigasheet MCP with OpenAI Agents SDK

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Introduction

This guide walks you through connecting Gigasheet to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Gigasheet agent that can list all columns in my sales dataset, download export url for last week's data, apply saved filter to monthly report sheet, show all filter templates in my workspace through natural language commands.

This guide will help you understand how to give your OpenAI Agents SDK agent real control over a Gigasheet account through Composio's Gigasheet 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 and set up your OpenAI and Composio API keys
  • Install the necessary dependencies
  • Initialize Composio and create a Tool Router session for Gigasheet
  • Configure an AI agent that can use Gigasheet as a tool
  • Run a live chat session where you can ask the agent to perform Gigasheet operations

What is open-ai-agents-sdk?

The OpenAI Agents SDK is a lightweight framework for building AI agents that can use tools and maintain conversation state. It provides a simple interface for creating agents with hosted MCP tool support.

Key features include:

  • Hosted MCP Tools: Connect to external services through hosted MCP endpoints
  • SQLite Sessions: Persist conversation history across interactions
  • Simple API: Clean interface with Agent, Runner, and tool configuration
  • Streaming Support: Real-time response streaming for interactive applications

What is the Gigasheet MCP server, and what's possible with it?

The Gigasheet MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Gigasheet account. It provides structured and secure access to your big data spreadsheets, so your agent can perform actions like retrieving datasets, applying filters, exporting data, managing sheets, and integrating with connector sources on your behalf.

  • Dataset retrieval and inspection: Instantly fetch metadata or details for any dataset or sheet, such as column names, types, and structure, so you can quickly understand and analyze your data.
  • Automated data export and download: Direct your agent to initiate data exports and retrieve download links for processed datasets, streamlining big data extraction directly to your tools or workflows.
  • Smart filtering and template application: Apply saved filter templates to sheets or retrieve available filter templates, enabling rapid, repeatable data curation without manual setup.
  • Sheet and folder management: Effortlessly delete sheets or folders—including recursive deletions—so you can keep your workspace organized and clutter-free.
  • Connector and integration management: List and manage connector connections to keep all your external data sources in sync with Gigasheet, making data aggregation seamless and automated.

Supported Tools & Triggers

Tools
Delete sheet or folder by handleTool to delete a sheet or folder by handle.
Get Client State Current VersionTool to fetch the current client-state version metadata for a sheet.
Get Connector ConnectionsTool to list connector connections.
Get Dataset by HandleTool to get dataset metadata.
Get Dataset ColumnsTool to list all column metadata (IDs, names, types) for a dataset.
Get Dataset Export Download URLTool to retrieve the download URL for an exported dataset.
Get Dataset ViewsTool to list all views associated with a specific dataset.
Get Docs Formulas FunctionsTool to retrieve all supported formula functions.
Apply Filter Template On SheetTool to fetch a saved filter template's model for a given sheet.
Get Filter TemplatesTool to retrieve all saved filter templates.
Generate New HandleTool to generate a new unique dataset handle.
Get User Autofill InfoTool to fetch autofill info for the authenticated user.
Get Authenticated User InfoTool to fetch the authenticated user's details.
Append Rows to Sheet by NameTool to append rows to a sheet by column names.
Initiate Dataset ExportTool to initiate an export of a dataset.
Insert Blank Row in DatasetTool to insert a blank row with null values into a dataset.
Rename Columns to UniqueTool to rename all columns in a dataset to unique names.
Save Current ViewTool to persist the current view in a Gigasheet dataset.
Get Filtered Row IndexTool to retrieve the filtered-set row index for a given unfiltered row number.
Combine Files by NameTool to combine multiple files by a shared column name.
Export Gigasheet to S3Tool to export Gigasheet data to AWS S3.
Import from S3Tool to import data from AWS S3 into your Gigasheet Library.
Request API AccessTool to request access to the Gigasheet API.
Unroll Delimited ColumnTool to explode a column containing delimited data into multiple rows.
Upload from URLTool to upload data to Gigasheet from a specified URL.
Set Dataset Client State VersionTool to set the client state version of a dataset.
Update cell by column name and rowTool to update a cell in a dataset by specifying column name and row number.
Share fileTool to share a Gigasheet file with specified recipients.
Create/Update Filter TemplateTool to create or update a saved filter template.

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:
  • Composio API Key and OpenAI API Key
  • Primary know-how of OpenAI Agents SDK
  • A live Gigasheet project
  • Some knowledge of Python or Typescript

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

Install dependencies

pip install composio_openai_agents openai-agents python-dotenv

Install the Composio SDK and the OpenAI Agents SDK.

Set up environment variables

bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com

Create a .env file and add your OpenAI and Composio API keys.

Import dependencies

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession
What's happening:
  • You're importing all necessary libraries.
  • The Composio and OpenAIAgentsProvider classes are imported to connect your OpenAI agent to Composio tools like Gigasheet.

Set up the Composio instance

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())
What's happening:
  • load_dotenv() loads your .env file so OPENAI_API_KEY and COMPOSIO_API_KEY are available as environment variables.
  • Creating a Composio instance using the API Key and OpenAIAgentsProvider class.

Create a Tool Router session

# Create a Gigasheet Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["gigasheet"]
)

mcp_url = session.mcp.url

What is happening:

  • You give the Tool Router the user id and the toolkits you want available. Here, it is only gigasheet.
  • The router checks the user's Gigasheet connection and prepares the MCP endpoint.
  • The returned session.mcp.url is the MCP URL that your agent will use to access Gigasheet.
  • This approach keeps things lightweight and lets the agent request Gigasheet tools only when needed during the conversation.

Configure the agent

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Gigasheet. "
        "Help users perform Gigasheet operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)
What's happening:
  • We're creating an Agent instance with a name, model (gpt-5), and clear instructions about its purpose.
  • The agent's instructions tell it that it can access Gigasheet and help with queries, inserts, updates, authentication, and fetching database information.
  • The tools array includes a HostedMCPTool that connects to the MCP server URL we created earlier.
  • The headers dict includes the Composio API key for secure authentication with the MCP server.
  • require_approval: 'never' means the agent can execute Gigasheet operations without asking for permission each time, making interactions smoother.

Start chat loop and handle conversation

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
What's happening:
  • The program prints a session URL that you visit to authorize Gigasheet.
  • After authorization, the chat begins.
  • Each message you type is processed by the agent using Runner.run().
  • The responses are printed to the console, and conversations are saved locally using SQLite.
  • Typing exit, quit, or q cleanly ends the chat.

Complete Code

Here's the complete code to get you started with Gigasheet and open-ai-agents-sdk:

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["gigasheet"]
)
mcp_url = session.mcp.url

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Gigasheet. "
        "Help users perform Gigasheet operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())

Conclusion

This was a starter code for integrating Gigasheet MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Gigasheet.

Key features:

  • Hosted MCP tool integration through Composio's Tool Router
  • SQLite session persistence for conversation history
  • Simple async chat loop for interactive testing
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

How to build Gigasheet MCP Agent with another framework

Claude Agent SDK

Claude Agent SDK

Use Gigasheet MCP with Claude Agent SDK

Google ADK

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Use Gigasheet MCP with Google ADK

LangChain

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Use Gigasheet MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Gigasheet MCP with Vercel AI SDK

Mastra AI

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Use Gigasheet MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Gigasheet MCP with LlamaIndex

CrewAI

CrewAI

Use Gigasheet MCP with CrewAI

FAQ

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

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

Can I use Tool Router MCP with OpenAI Agents SDK?

Yes, you can. OpenAI Agents SDK 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 Gigasheet tools.

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

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

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