# How to integrate Excel MCP with LlamaIndex

```json
{
  "title": "How to integrate Excel MCP with LlamaIndex",
  "toolkit": "Excel",
  "toolkit_slug": "excel",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/excel/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/excel/framework/llama-index.md",
  "updated_at": "2026-05-12T10:10:57.727Z"
}
```

## Introduction

This guide walks you through connecting Excel to LlamaIndex using the Composio tool router. By the end, you'll have a working Excel agent that can add sales data row to q2 table, create bar chart from revenue column, share this workbook with your manager through natural language commands.
This guide will help you understand how to give your LlamaIndex agent real control over a Excel account through Composio's Excel MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Excel with

- [ChatGPT](https://composio.dev/toolkits/excel/framework/chatgpt)
- [OpenAI Agents SDK](https://composio.dev/toolkits/excel/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/excel/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/excel/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/excel/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/excel/framework/codex)
- [Cursor](https://composio.dev/toolkits/excel/framework/cursor)
- [VS Code](https://composio.dev/toolkits/excel/framework/vscode)
- [OpenCode](https://composio.dev/toolkits/excel/framework/opencode)
- [OpenClaw](https://composio.dev/toolkits/excel/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/excel/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/excel/framework/cli)
- [Google ADK](https://composio.dev/toolkits/excel/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/excel/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/excel/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/excel/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/excel/framework/crew-ai)

## 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 Excel
- Connect LlamaIndex to the Excel MCP server
- Build a Excel-powered agent using LlamaIndex
- Interact with Excel 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 Excel MCP server, and what's possible with it?

The Excel MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Excel account. It provides structured and secure access to your spreadsheets, so your agent can perform actions like adding data, creating tables, managing worksheets, generating charts, and sharing workbooks on your behalf.
- Automated data entry and updates: Let your agent add rows, columns, or clear specific ranges in any worksheet—keeping your data fresh, organized, and accurate.
- Effortless table and worksheet management: Direct your agent to create tables, add new worksheets, or organize data structures for seamless tracking and reporting.
- Dynamic chart generation: Have your agent visualize your data instantly by adding charts to any worksheet for quick insights and analysis.
- Advanced filtering and sorting: Ask your agent to apply filters or custom sorts to tables, making it easy to focus on what matters most in your datasets.
- Secure sharing and permission control: Empower your agent to grant access or update permissions on workbooks, ensuring your team can collaborate safely and efficiently.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `EXCEL_ADD_CHART` | Add Chart | Add a chart to a worksheet using Microsoft Graph API. |
| `EXCEL_ADD_SHAREPOINT_WORKSHEET` | Add SharePoint Worksheet | Add a new worksheet to a SharePoint Excel workbook using Microsoft Graph Sites API. |
| `EXCEL_ADD_TABLE` | Add Table | Create a new table in a worksheet using the Microsoft Graph API. |
| `EXCEL_ADD_TABLE_COLUMN` | Add Table Column | Add a column to a table using Microsoft Graph API. |
| `EXCEL_ADD_TABLE_ROW` | Add Table Row | Add a row to a table using Microsoft Graph API. |
| `EXCEL_ADD_WORKBOOK_PERMISSION` | Add Workbook Permission | Tool to grant access to a workbook via invite. Use when you need to share a specific workbook file with designated recipients and roles. |
| `EXCEL_ADD_WORKSHEET` | Add Worksheet | Add a new worksheet to an Excel workbook using Microsoft Graph API. |
| `EXCEL_APPLY_TABLE_FILTER` | Apply Table Filter | Apply a filter to a table column using Microsoft Graph API. |
| `EXCEL_APPLY_TABLE_SORT` | Apply Table Sort | Apply a sort to a table using Microsoft Graph API. |
| `EXCEL_CLEAR_RANGE` | Clear Range | Tool to clear values, formats, or contents in a specified worksheet range. Use when you need to reset cells before adding new data. |
| `EXCEL_CLEAR_TABLE_FILTER` | Clear Table Filter | Clear a filter from a table column using Microsoft Graph API. |
| `EXCEL_CLOSE_SESSION` | Close Excel Session | Tool to close an existing Excel workbook session. Use when you need to explicitly end a persistent session to release workbook locks. Note: The Microsoft Graph closeSession API is idempotent - it returns 204 for both active and already-closed sessions. This action validates the session first and returns an error for invalid or already-closed sessions to provide clearer user feedback. The validation uses refreshSession which is the only API endpoint that can detect closed sessions. |
| `EXCEL_CONVERT_TABLE_TO_RANGE` | Convert Table To Range | Convert a table to a range using Microsoft Graph API. |
| `EXCEL_CREATE_WORKBOOK` | Create Workbook | Tool to create a new Excel workbook file at a specified drive path. Generates a new .xlsx file with specified worksheets and data, then uploads it to OneDrive. |
| `EXCEL_DELETE_TABLE_COLUMN` | Delete Table Column | Delete a column from a table using Microsoft Graph API. |
| `EXCEL_DELETE_TABLE_ROW` | Delete Table Row | Delete a row from a table using Microsoft Graph API. |
| `EXCEL_DELETE_WORKSHEET` | Delete Worksheet | Tool to delete a worksheet from the workbook. Use when cleaning up unused or temporary sheets after verifying no dependencies exist. Example: "Delete 'Sheet2' after review." |
| `EXCEL_EXPORT_WORKBOOK_TO_PDF` | Export Workbook to PDF | Tool to export an Excel workbook to PDF via Microsoft Graph's format conversion. Use when you need a PDF version of an Excel file for sending, storing, or attaching. |
| `EXCEL_GET_CHART_AXIS` | Get Chart Axis | Tool to retrieve a specific axis from a chart. Use when you need properties like min, max, interval, and formatting of the chart axis. |
| `EXCEL_GET_CHART_DATA_LABELS` | Get Chart Data Labels | Tool to retrieve the data labels object of a chart. Use when you need to inspect label settings like position, separator, and visibility flags after creating or updating a chart. |
| `EXCEL_GET_CHART_LEGEND` | Get Chart Legend | Tool to retrieve the legend object of a chart. Use after creating or updating a chart when you need to inspect legend visibility and formatting. |
| `EXCEL_GET_RANGE` | Get Range | Get a range from a worksheet using Microsoft Graph API. |
| `EXCEL_GET_SESSION` | Create Excel Session | Create a session for an Excel workbook using Microsoft Graph API. |
| `EXCEL_GET_SHAREPOINT_RANGE` | Get SharePoint Range | Get a range from a worksheet in SharePoint using Microsoft Graph Sites API. |
| `EXCEL_GET_SHAREPOINT_WORKSHEET` | Get SharePoint Worksheet | Get a worksheet by name or ID from a SharePoint Excel workbook using Microsoft Graph Sites API. |
| `EXCEL_GET_TABLE_COLUMN` | Get table column | Tool to retrieve a specific column from a workbook table. Use when you need to fetch column properties and data by its ID or name. |
| `EXCEL_GET_WORKBOOK` | Get workbook | Tool to retrieve the properties and relationships of a workbook. Use when you need to inspect comments, names, tables, or worksheets. |
| `EXCEL_GET_WORKSHEET` | Get Worksheet | Get a worksheet by name or ID from an Excel workbook using Microsoft Graph API. |
| `EXCEL_GET_WORKSHEET_USED_RANGE` | Get Worksheet Used Range | Tool to retrieve a worksheet's used range (active data region) without specifying a fixed range address. Use when you need to read all data from a sheet but don't know the exact range. The valuesOnly option helps filter out formatting-only cells. |
| `EXCEL_INSERT_RANGE` | Insert Range | Tool to insert a new cell range into a worksheet, shifting existing cells down or right. Use when you need to create space for new content without overwriting. |
| `EXCEL_LIST_CHARTS` | List Charts | List charts in a worksheet using Microsoft Graph API. |
| `EXCEL_LIST_CHART_SERIES` | List Chart Series | Tool to list all data series in a chart. Use when you need to enumerate chart series for further analysis. |
| `EXCEL_LIST_COMMENTS` | List Comments | Tool to list comments in an Excel workbook. Use when you need to retrieve all workbook comments via Microsoft Graph API. |
| `EXCEL_LIST_DRIVE_ITEM_CHILDREN` | List Drive Item Children | Tool to list immediate children (files/folders) of a folder DriveItem using driveId and itemId. Returns an array of child DriveItems with stable identifiers and pagination support. |
| `EXCEL_LIST_FILES` | List Drive Files | List files and folders in a drive root or specified path. |
| `EXCEL_LIST_NAMED_ITEMS` | List Named Items | List named items in a workbook using Microsoft Graph API. |
| `EXCEL_LIST_SHAREPOINT_TABLES` | List SharePoint Tables | List tables in a SharePoint worksheet using Microsoft Graph Sites API. |
| `EXCEL_LIST_SHAREPOINT_WORKSHEETS` | List SharePoint Worksheets | List worksheets in an Excel workbook stored in SharePoint using Microsoft Graph Sites API. |
| `EXCEL_LIST_TABLE_COLUMNS` | List Table Columns | List columns in a table using Microsoft Graph API. |
| `EXCEL_LIST_TABLE_ROWS` | List Table Rows | List rows in a table using Microsoft Graph API. |
| `EXCEL_LIST_TABLES` | List Tables | List tables in a worksheet using Microsoft Graph API. This action retrieves information about all tables present in a specified worksheet of an Excel file. It requires the file ID and worksheet name or ID, and can optionally use a session ID for workbook operations. |
| `EXCEL_LIST_WORKBOOK_PERMISSIONS` | List Workbook Permissions | Tool to list permissions set on the workbook file. Use when you need to see which users or links have access to a specific Excel file by supplying its drive and item IDs. Example: "List permissions for workbook with drive_id 'b!abc123' and item_id '0123456789abcdef'." |
| `EXCEL_LIST_WORKSHEETS` | List Worksheets | List worksheets in an Excel workbook using Microsoft Graph API. |
| `EXCEL_MERGE_CELLS` | Merge Cells | Merge cells in a worksheet range using Microsoft Graph API. |
| `EXCEL_PROTECT_WORKSHEET` | Protect Worksheet | Tool to protect a worksheet using optional protection options. Use when you need to prevent editing certain parts of a sheet before sharing. Example: "Protect 'Sheet1' to lock formatting and sorting." |
| `EXCEL_SEARCH_FILES` | Search Drive Files | Tool to search OneDrive drive items by query to discover Excel workbook IDs. Use when you need to find Excel files by name before performing workbook operations. |
| `EXCEL_SORT_RANGE` | Sort Range | Sort a range in a worksheet using Microsoft Graph API. |
| `EXCEL_UPDATE_CHART` | Update Chart | Update a chart in a worksheet using Microsoft Graph API. |
| `EXCEL_UPDATE_CHART_LEGEND` | Update Chart Legend | Tool to update formatting or position of a chart legend. Use when adjusting legend settings after confirming chart and worksheet exist. |
| `EXCEL_UPDATE_RANGE` | Update Range | Update a range in a worksheet using Microsoft Graph API. |
| `EXCEL_UPDATE_SHAREPOINT_RANGE` | Update SharePoint Range | Update a range in a SharePoint worksheet using Microsoft Graph Sites API. |
| `EXCEL_UPDATE_TABLE` | Update Table | Update a table in a workbook using Microsoft Graph API. |
| `EXCEL_UPDATE_WORKSHEET` | Update Worksheet | Update worksheet properties (name, position) in an Excel workbook using Microsoft Graph API. |
| `EXCEL_UPLOAD_WORKBOOK` | Upload Workbook from URL | Tool to upload an external Excel file from a URL into OneDrive/SharePoint. Downloads the file server-side and uploads it to the specified drive location, returning the driveItem metadata for subsequent Excel operations. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Excel MCP server is an implementation of the Model Context Protocol that connects your AI agent to Excel. It provides structured and secure access so your agent can perform Excel operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. 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 Excel account and project
- Basic familiarity with async Python/Typescript

### 1. Getting API Keys for OpenAI, Composio, and Excel

No description provided.

### 2. Installing dependencies

No description provided.
```python
pip install composio-llamaindex llama-index llama-index-llms-openai llama-index-tools-mcp python-dotenv
```

```typescript
npm install @composio/llamaindex @llamaindex/openai @llamaindex/tools @llamaindex/workflow dotenv
```

### 3. Set environment variables

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

### 4. Import modules

No description provided.
```python
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()
```

```typescript
import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();
```

### 5. Load environment variables and initialize Composio

No description provided.
```python
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")
```

```typescript
const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!COMPOSIO_API_KEY) throw new Error("COMPOSIO_API_KEY is not set");
if (!COMPOSIO_USER_ID) throw new Error("COMPOSIO_USER_ID is not set");
```

### 6. Create a Tool Router session and build the agent function

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, excel)
- 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 Excel tools.
- The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.
```python
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=["excel"],
    )

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

```typescript
async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["excel"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
        description : "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform Excel actions." ,
    llm,
    tools,
  });

  return agent;
}
```

### 7. Create an interactive chat loop

No description provided.
```python
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}")
```

```typescript
async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}
```

### 8. Define the main entry point

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 Excel
```python
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!")
```

```typescript
async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err) {
    console.error("Failed to start agent:", err);
    process.exit(1);
  }
}

main();
```

### 9. Run the agent

When prompted, authenticate and authorise your agent with Excel, then start asking questions.
```bash
python llamaindex_agent.py
```

```typescript
npx ts-node llamaindex-agent.ts
```

## Complete Code

```python
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=["excel"],
    )

    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 Excel actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Excel 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!")
```

```typescript
import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";
import { LlamaindexProvider } from "@composio/llamaindex";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();

const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) {
    throw new Error("OPENAI_API_KEY is not set in the environment");
  }
if (!COMPOSIO_API_KEY) {
    throw new Error("COMPOSIO_API_KEY is not set in the environment");
  }
if (!COMPOSIO_USER_ID) {
    throw new Error("COMPOSIO_USER_ID is not set in the environment");
  }

async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["excel"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
    description:
      "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform Excel actions." ,
    llm,
    tools,
  });

  return agent;
}

async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}

async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err: any) {
    console.error("Failed to start agent:", err?.message ?? err);
    process.exit(1);
  }
}

main();
```

## Conclusion

You've successfully connected Excel to LlamaIndex through Composio's Tool Router MCP layer.
Key takeaways:
- Tool Router dynamically exposes Excel 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 Excel MCP Agent with another framework

- [ChatGPT](https://composio.dev/toolkits/excel/framework/chatgpt)
- [OpenAI Agents SDK](https://composio.dev/toolkits/excel/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/excel/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/excel/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/excel/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/excel/framework/codex)
- [Cursor](https://composio.dev/toolkits/excel/framework/cursor)
- [VS Code](https://composio.dev/toolkits/excel/framework/vscode)
- [OpenCode](https://composio.dev/toolkits/excel/framework/opencode)
- [OpenClaw](https://composio.dev/toolkits/excel/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/excel/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/excel/framework/cli)
- [Google ADK](https://composio.dev/toolkits/excel/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/excel/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/excel/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/excel/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/excel/framework/crew-ai)

## Related Toolkits

- [21risk](https://composio.dev/toolkits/_21risk) - 21RISK is a web app built for easy checklist, audit, and compliance management. It streamlines risk processes so teams can focus on what matters.
- [Abstract](https://composio.dev/toolkits/abstract) - Abstract provides a suite of APIs for automating data validation and enrichment tasks. It helps developers streamline workflows and ensure data quality with minimal effort.
- [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time.
- [Agentql](https://composio.dev/toolkits/agentql) - Agentql is a toolkit that connects AI agents to the web using a specialized query language. It enables structured web interaction and data extraction for smarter automations.
- [Agenty](https://composio.dev/toolkits/agenty) - Agenty is a web scraping and automation platform for extracting data and automating browser tasks—no coding needed. It streamlines data collection, monitoring, and repetitive online actions.
- [Ambee](https://composio.dev/toolkits/ambee) - Ambee is an environmental data platform providing real-time, hyperlocal APIs for air quality, weather, and pollen. Get precise environmental insights to power smarter decisions in your apps and workflows.
- [Ambient weather](https://composio.dev/toolkits/ambient_weather) - Ambient Weather is a platform for personal weather stations with a robust API for accessing local, real-time, and historical weather data. Get detailed environmental insights directly from your own sensors for smarter apps and automations.
- [Anonyflow](https://composio.dev/toolkits/anonyflow) - Anonyflow is a service for encryption-based data anonymization and secure data sharing. It helps organizations meet GDPR, CCPA, and HIPAA data privacy compliance requirements.
- [Api ninjas](https://composio.dev/toolkits/api_ninjas) - Api ninjas offers 120+ public APIs spanning categories like weather, finance, sports, and more. Developers use it to supercharge apps with real-time data and actionable endpoints.
- [Api sports](https://composio.dev/toolkits/api_sports) - Api sports is a comprehensive sports data platform covering 2,000+ competitions with live scores and 15+ years of stats. Instantly access up-to-date sports information for analysis, apps, or chatbots.
- [Apify](https://composio.dev/toolkits/apify) - Apify is a cloud platform for building, deploying, and managing web scraping and automation tools called Actors. It lets you automate data extraction and workflow tasks at scale—no infrastructure headaches.
- [Autom](https://composio.dev/toolkits/autom) - Autom is a lightning-fast search engine results data platform for Google, Bing, and Brave. Developers use it to access fresh, low-latency SERP data on demand.
- [Beaconchain](https://composio.dev/toolkits/beaconchain) - Beaconchain is a real-time analytics platform for Ethereum 2.0's Beacon Chain. It provides detailed insights into validators, blocks, and overall network performance.
- [Big data cloud](https://composio.dev/toolkits/big_data_cloud) - BigDataCloud provides APIs for geolocation, reverse geocoding, and address validation. Instantly access reliable location intelligence to enhance your applications and workflows.
- [Bigpicture io](https://composio.dev/toolkits/bigpicture_io) - BigPicture.io offers APIs for accessing detailed company and profile data. Instantly enrich your applications with up-to-date insights on 20M+ businesses.
- [Bitquery](https://composio.dev/toolkits/bitquery) - Bitquery is a blockchain data platform offering indexed, real-time, and historical data from 40+ blockchains via GraphQL APIs. Get unified, reliable access to complex on-chain data for analytics, trading, and research.
- [Brightdata](https://composio.dev/toolkits/brightdata) - Brightdata is a leading web data platform offering advanced scraping, SERP APIs, and anti-bot tools. It lets you collect public web data at scale, bypassing blocks and friction.
- [Builtwith](https://composio.dev/toolkits/builtwith) - BuiltWith is a web technology profiler that uncovers the technologies powering any website. Gain actionable insights into analytics, hosting, and content management stacks for smarter research and lead generation.
- [Byteforms](https://composio.dev/toolkits/byteforms) - Byteforms is an all-in-one platform for creating forms, managing submissions, and integrating data. It streamlines workflows by centralizing form data collection and automation.
- [Cabinpanda](https://composio.dev/toolkits/cabinpanda) - Cabinpanda is a data collection platform for building and managing online forms. It helps streamline how you gather, organize, and analyze responses.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Excel MCP?

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

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

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

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)