How to integrate One drive MCP with LlamaIndex

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Introduction

This guide walks you through connecting One drive to LlamaIndex using the Composio tool router. By the end, you'll have a working One drive agent that can share project folder with my team, download the latest version of report.docx, check who can access budget.xlsx, list my remaining onedrive storage space through natural language commands.

This guide will help you understand how to give your LlamaIndex agent real control over a One drive account through Composio's One drive 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 One drive
  • Connect LlamaIndex to the One drive MCP server
  • Build a One drive-powered agent using LlamaIndex
  • Interact with One drive 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 One drive MCP server, and what's possible with it?

The One drive MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your OneDrive account. It provides structured and secure access to your cloud files and folders, so your agent can perform actions like uploading documents, sharing files, managing storage, and retrieving version histories on your behalf.

  • File and folder management: Effortlessly copy, move, or delete files and folders, keeping your OneDrive organized with just a prompt.
  • Easy sharing and collaboration: Instantly generate secure sharing links for documents or folders, making collaboration with others seamless.
  • File download and preview: Have your agent fetch files or retrieve visual thumbnails for quick previews and streamlined access.
  • Access control and permissions review: Check who can view or edit any file or folder, and manage sharing permissions without manual clicks.
  • Version tracking and quota monitoring: Retrieve version histories for files and monitor your storage quota to stay on top of changes and space usage.

Supported Tools & Triggers

Tools
Triggers
Copy ItemTool to copy a driveitem (file or folder) to a new location asynchronously.
Create Sharing LinkTool to create a sharing link for a driveitem (file or folder) by its unique id.
Delete ItemTool to delete a driveitem (file or folder) by its unique id from the authenticated user's onedrive.
Download a fileDownloads a file from a user's onedrive using its item id, which must refer to a file and not a folder.
Get DriveRetrieves the properties and relationships of a drive resource by its unique id.
Get Item MetadataRetrieves the metadata of a driveitem by its unique id.
Get Item PermissionsRetrieves the permissions of a driveitem by its unique id or path within a specific drive.
Get Item ThumbnailsTool to retrieve the thumbnails associated with a driveitem.
Get Item VersionsTool to retrieve the version history of a driveitem by its unique id.
Get Drive QuotaTool to retrieve the quota information for the authenticated user's onedrive.
Get Recent ItemsRetrieves a list of items that have been recently used by the authenticated user.
Get Shared ItemsTool to retrieve a list of items that have been shared with the authenticated user.
Get SharePoint List ItemsTool to get the items (list items) within a specific sharepoint list on a site.
Get Site DetailsRetrieves metadata for a specific sharepoint site by its id.
Get SharePoint Site Page ContentGets the content of a modern sharepoint site page.
Invite User to Drive ItemTool to invite users or grant permissions to a specific item in a onedrive drive.
List Drive Item ActivitiesTool to list recent activities for a specific item in a onedrive drive.
List DrivesTool to retrieve a list of drive resources available to the authenticated user, or for a specific user, group, or site.
List Root Drive ChangesTool to list changes in the root of the user's primary drive using a delta token.
List SharePoint List Items DeltaTool to track changes to items in a sharepoint list using a delta query.
List Site ColumnsTool to list all column definitions for a sharepoint site.
List Site Drive Items DeltaTool to track changes to driveitems in the default document library of a sharepoint site.
List Site ListsTool to list all lists under a specific sharepoint site.
List Site SubsitesTool to list all subsites of a sharepoint site.
List SubscriptionsTool to list the current subscriptions for the authenticated user or app.
Move ItemTool to move a file or folder to a new parent folder in onedrive.
Create folderCreates a new folder in the user's onedrive, automatically renaming on conflict, optionally within a specified parent folder (by id or full path from root) which, if not the root, must exist and be accessible.
Create a new text fileCreates a new text file with specified content in a onedrive folder, using either the folder's unique id or its absolute path (paths are automatically resolved to ids); note that onedrive may rename or create a new version if the filename already exists.
Find ItemNon-recursively finds an item (file or folder) in a specified onedrive folder; if `folder` is provided as a path, it must actually exist.
Find FolderFinds folders by name within an accessible parent folder in onedrive, or lists all its direct child folders if no name is specified.
List OneDrive itemsRetrieves all files and folders as `driveitem` resources from the root of a specified user's onedrive, automatically handling pagination.
Upload fileUploads a file to a specified onedrive folder, automatically renaming on conflict and supporting large files via chunking.
Preview Drive ItemGenerates or retrieves a short-lived embeddable url for a preview of a specific item.
Search ItemsSearches for driveitems in onedrive that match the specified query.
Update Drive Item MetadataTool to update the metadata of a specific item (file or folder) in onedrive.

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 One drive account and project
  • Basic familiarity with async Python/Typescript

Getting API Keys for OpenAI, Composio, and One drive

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 One drive 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 one drive_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=["one_drive"],
    )

    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 One drive actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform One drive 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, one drive)
  • 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 One drive 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 One drive 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 One drive

Run the agent

npx ts-node llamaindex-agent.ts

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

Complete Code

Here's the complete code to get you started with One drive 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=["one_drive"],
    )

    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 One drive actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform One drive 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 One drive to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways:
  • Tool Router dynamically exposes One drive 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 One drive MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use One drive MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use One drive MCP with Claude Agent SDK

Google ADK

Google ADK

Use One drive MCP with Google ADK

LangChain

LangChain

Use One drive MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use One drive MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use One drive MCP with Mastra AI

CrewAI

CrewAI

Use One drive MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and One drive MCP?

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

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

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

Used by agents from

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Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
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Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai

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