How to integrate Gigasheet MCP with LlamaIndex

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Introduction

This guide walks you through connecting Gigasheet to LlamaIndex 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 LlamaIndex 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:
  • Set your OpenAI and Composio API keys
  • Install LlamaIndex and Composio packages
  • Create a Composio Tool Router session for Gigasheet
  • Connect LlamaIndex to the Gigasheet MCP server
  • Build a Gigasheet-powered agent using LlamaIndex
  • Interact with Gigasheet through natural language

What is LlamaIndex?

LlamaIndex is a data framework for building LLM applications. It provides tools for connecting LLMs to external data sources and services through agents and tools.

Key features include:

  • ReAct Agent: Reasoning and acting pattern for tool-using agents
  • MCP Tools: Native support for Model Context Protocol
  • Context Management: Maintain conversation context across interactions
  • Async Support: Built for async/await patterns

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 you begin, make sure you have:
  • Python 3.8/Node 16 or higher installed
  • A Composio account with the API key
  • An OpenAI API key
  • A Gigasheet account and project
  • Basic familiarity with async Python/Typescript

Getting API Keys for OpenAI, Composio, and Gigasheet

OpenAI API key (OPENAI_API_KEY)
  • Go to the OpenAI dashboard
  • Create an API key if you don't have one
  • Assign it to OPENAI_API_KEY in .env
Composio API key and user ID
  • Log into the Composio dashboard
  • Copy your API key from Settings
    • Use this as COMPOSIO_API_KEY
  • Pick a stable user identifier (email or ID)
    • Use this as COMPOSIO_USER_ID

Installing dependencies

pip install composio-llamaindex llama-index llama-index-llms-openai llama-index-tools-mcp python-dotenv

Create a new Python project and install the necessary dependencies:

  • composio-llamaindex: Composio's LlamaIndex integration
  • llama-index: Core LlamaIndex framework
  • llama-index-llms-openai: OpenAI LLM integration
  • llama-index-tools-mcp: MCP client for LlamaIndex
  • python-dotenv: Environment variable management

Set environment variables

bash
OPENAI_API_KEY=your-openai-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_USER_ID=your-user-id

Create a .env file in your project root:

These credentials will be used to:

  • Authenticate with OpenAI's GPT-5 model
  • Connect to Composio's Tool Router
  • Identify your Composio user session for Gigasheet access

Import modules

import asyncio
import os
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

Create a new file called gigasheet_llamaindex_agent.py and import the required modules:

Key imports:

  • asyncio: For async/await support
  • Composio: Main client for Composio services
  • LlamaIndexProvider: Adapts Composio tools for LlamaIndex
  • ReActAgent: LlamaIndex's reasoning and action agent
  • BasicMCPClient: Connects to MCP endpoints
  • McpToolSpec: Converts MCP tools to LlamaIndex format

Load environment variables and initialize Composio

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_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")

What's happening:

This ensures missing credentials cause early, clear errors before the agent attempts to initialise.

Create a Tool Router session and build the agent function

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["gigasheet"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")

    description = "An agent that uses Composio Tool Router MCP tools to perform Gigasheet actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Gigasheet actions.
    """
    return ReActAgent(tools=tools, llm=llm, description=description, system_prompt=system_prompt, verbose=True)

What's happening here:

  • We create a Composio client using your API key and configure it with the LlamaIndex provider
  • We then create a tool router MCP session for your user, specifying the toolkits we want to use (in this case, gigasheet)
  • The session returns an MCP HTTP endpoint URL that acts as a gateway to all your configured tools
  • LlamaIndex will connect to this endpoint to dynamically discover and use the available Gigasheet tools.
  • The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.

Create an interactive chat loop

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

What's happening here:

  • We're creating a direct terminal interface to chat with your Gigasheet database
  • The LLM's responses are streamed to the CLI for faster interaction.
  • The agent uses context to maintain conversation history
  • You can type 'quit' or 'exit' to stop the chat loop gracefully
  • Agent responses and any errors are displayed in a clear, readable format

Define the main entry point

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")

What's happening here:

  • We're orchestrating the entire application flow
  • The agent gets built with proper error handling
  • Then we kick off the interactive chat loop so you can start talking to Gigasheet

Run the agent

npx ts-node llamaindex-agent.ts

When prompted, authenticate and authorise your agent with Gigasheet, then start asking questions.

Complete Code

Here's the complete code to get you started with Gigasheet and LlamaIndex:

import asyncio
import os
import signal
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set")
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")

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["gigasheet"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")
    description = "An agent that uses Composio Tool Router MCP tools to perform Gigasheet actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Gigasheet actions.
    """
    return ReActAgent(
        tools=tools,
        llm=llm,
        description=description,
        system_prompt=system_prompt,
        verbose=True,
    );

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")

Conclusion

You've successfully connected Gigasheet to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways:
  • Tool Router dynamically exposes Gigasheet tools through an MCP endpoint
  • LlamaIndex's ReActAgent handles reasoning and orchestration; Composio handles integrations
  • The agent becomes more capable without increasing prompt size
  • Async Python provides clean, efficient execution of agent workflows
You can easily extend this to other toolkits like Gmail, Notion, Stripe, GitHub, and more by adding them to the toolkits parameter.

How to build Gigasheet MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Gigasheet MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Gigasheet MCP with Claude Agent SDK

Google ADK

Google ADK

Use Gigasheet MCP with Google ADK

LangChain

LangChain

Use Gigasheet MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Gigasheet MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Gigasheet MCP with Mastra AI

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 LlamaIndex?

Yes, you can. LlamaIndex 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.

Used by agents from

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