# How to integrate Unione MCP with LlamaIndex

```json
{
  "title": "How to integrate Unione MCP with LlamaIndex",
  "toolkit": "Unione",
  "toolkit_slug": "unione",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/unione/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/unione/framework/llama-index.md",
  "updated_at": "2026-05-06T08:32:44.085Z"
}
```

## Introduction

This guide walks you through connecting Unione to LlamaIndex using the Composio tool router. By the end, you'll have a working Unione agent that can check your current unione email balance, cancel a scheduled email by job id, list all sender domains and their status through natural language commands.
This guide will help you understand how to give your LlamaIndex agent real control over a Unione account through Composio's Unione MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Unione with

- [OpenAI Agents SDK](https://composio.dev/toolkits/unione/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/unione/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/unione/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/unione/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/unione/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/unione/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/unione/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/unione/framework/cli)
- [Google ADK](https://composio.dev/toolkits/unione/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/unione/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/unione/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/unione/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/unione/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 Unione
- Connect LlamaIndex to the Unione MCP server
- Build a Unione-powered agent using LlamaIndex
- Interact with Unione 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 Unione MCP server, and what's possible with it?

The Unione MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Unione account. It provides structured and secure access to your Unione email delivery service, so your agent can send transactional or marketing emails, manage sending domains, monitor delivery events, check account balance, and automate email operations on your behalf.
- Automated email sending and scheduling: Have your agent send transactional or marketing emails and even schedule deliveries right from your Unione account.
- Domain verification and management: Easily manage sender domains, trigger domain verifications, and handle DNS/DKIM checks to keep your emails deliverable.
- Event monitoring and export: Let your agent fetch specific email events, retrieve delivery metrics, or export comprehensive email event logs for auditing and analytics.
- Account balance and plan checks: Quickly access your current email balance and subscription plan details, ensuring you stay within your sending limits.
- Email job and pricing insights: Retrieve detailed information about specific email jobs and get up-to-date pricing for cost management before sending campaigns.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `UNIONE_EMAIL_BALANCE` | UniOne Email Balance | Tool to retrieve current account balance. use when you need to check your email usage and limits before sending large campaigns. |
| `UNIONE_EMAIL_CANCEL` | Cancel Scheduled Email | Tool to cancel a scheduled transactional email by its job id. use when you need to stop a pending email send before it's dispatched. |
| `UNIONE_EMAIL_DOMAIN` | UniOne Email Domain Management | Tool to manage sender domains in unione. use when you need dns records for verification, trigger verification or dkim checks, list domains, or delete a domain. |
| `UNIONE_EMAIL_EVENT_GET` | Get Email Event | Tool to retrieve details of a specific email event by its id. use when you need event information for auditing or diagnostics. |
| `UNIONE_EMAIL_EVENT_TYPES` | UniOne Email Event Types | Tool to retrieve supported email event types. use when you need a list of possible event codes for filtering or analytics. |
| `UNIONE_EMAIL_GET` | Get Email Send Job | Tool to retrieve detailed information about a specific email send job. use when you need its delivery metrics and history. |
| `UNIONE_EMAIL_LIST` | UniOne Email List (Export) | Tool to export email events within a specified time frame. it creates an asynchronous event dump which can later be downloaded and parsed using unione event dump get. |
| `UNIONE_EMAIL_LOG` | UniOne Email Event Log | Tool to initiate an asynchronous export of email events (event dump). use when you need to export transactional email events for a specified time window. |
| `UNIONE_EMAIL_PLAN` | UniOne Email Plan | Tool to retrieve current subscription plan details. use when you need to check your project and account plan limits before sending bulk emails. |
| `UNIONE_EMAIL_PRICING` | UniOne Email Pricing | Tool to retrieve current email pricing. use when you need to check per-email cost rates before sending emails. |
| `UNIONE_EMAIL_QUOTA` | UniOne Email Quota | Tool to retrieve current email sending quota. use when you need to check your remaining quota before sending emails. |
| `UNIONE_EMAIL_RESEND` | Resend Sent Email | Tool to resend a previously sent email by its job id. use when you need to trigger a resend of an email that has already been sent and you have the original job id. |
| `UNIONE_EMAIL_RESUBSCRIBE` | UniOne Email Resubscribe | Tool to resubscribe a recipient who previously unsubscribed. use when you need to restore a user's subscription status after they opt in again. |
| `UNIONE_EMAIL_RESUME` | Resume Paused Email | Tool to resume a paused transactional email by its job id. use when you need to restart a paused pending email send. |
| `UNIONE_EMAIL_SCHEDULE` | UniOne Email Schedule | Tool to schedule a transactional email up to 24 hours ahead. use when you need to send an email at a specific future time. |
| `UNIONE_EMAIL_SMTP` | UniOne Email SMTP Configuration | Tool to retrieve smtp server details and credentials. use when you need to configure your mail client or library for smtp sending. |
| `UNIONE_EMAIL_STATISTICS` | UniOne Email Statistics | Tool to retrieve email sending statistics over a specified time range. this action uses unione's event-dump aggregate api under the hood to compute daily statistics. |
| `UNIONE_EMAIL_UNSUBSCRIBE` | UniOne Email Unsubscribe | Tool to unsubscribe an email from future emails. use when you need to stop all further transactional emails. |
| `UNIONE_EMAIL_VALIDATE` | Validate Email Address | Tool to validate an email address. use when you need deliverability diagnostics after compiling your recipient list. |
| `UNIONE_EMAIL_VALIDATE_BATCH` | Batch Email Validation | Tool to validate multiple email addresses in a batch. use when you need to verify deliverability for a list of emails at once. |
| `UNIONE_EMAIL_VALIDATE_RESEND` | Resend Email Validation Results | Tool to resend results of an email validation request. use when you need to retrieve validation results again by request id. |
| `UNIONE_EMAIL_VALIDATE_RESULT` | UniOne Email Validate Result | Tool to retrieve the detailed result of an email validation request. updated behavior: uses the official single email validation endpoint to synchronously obtain full diagnostics for the provided email address. |
| `UNIONE_EMAIL_VALIDATE_RETRY` | Retry Email Validation | Tool to retry an email validation request. updated to re-run validation via the official single validation endpoint using the provided email address. |
| `UNIONE_EMAIL_VALIDATE_STATUS` | UniOne Email Validate Status | Tool to retrieve the current status of an email validation request. use when you need to poll for completion status. |
| `UNIONE_EMAIL_WEBHOOK_TYPES` | UniOne Email Webhook Types | Tool to retrieve supported email webhook event types. use when configuring your webhook callbacks. |
| `UNIONE_EVENT_DUMP_CREATE` | Create Event Dump | Tool to create an asynchronous csv event dump. use when you need to export transactional email events for a specified time window. |
| `UNIONE_EVENT_DUMP_LIST` | UniOne Event Dump List | Tool to retrieve the full list of event dumps. use when you need to view all existing event-dump tasks. |
| `UNIONE_SCHEDULE_EMAIL` | Schedule Email | Tool to schedule a transactional email up to 24 hours ahead. use when you need to send an email at a specific future time. |
| `UNIONE_SUPPRESSION_LIST` | Suppression List | Tool to return the suppression list since a given date. use when auditing bounced, unsubscribed, or blocked recipients. |
| `UNIONE_TAG_DELETE` | Delete Tag | Tool to delete a specific tag. use when you have confirmed the tag id you wish to remove. |
| `UNIONE_TAG_LIST` | UniOne Tag List | Tool to retrieve all user-defined tags. use when you need to fetch the full list of tags after authentication. |
| `UNIONE_TEMPLATE_LIST` | UniOne Template List | Tool to list email templates. use when you need to retrieve available templates for transactional emails. |
| `UNIONE_TEMPLATE_SET` | Set Template | Tool to set or update an email template. use when you need to create or modify transactional email templates before sending messages. |
| `UNIONE_UNIONE_EMAIL_VALIDATE_DELETE` | Delete Email Validation Request | Tool to delete an email validation request. use when a validation job should be canceled by its id. |
| `UNIONE_UNIONE_EVENT_DUMP_GET` | Get Event Dump | Tool to retrieve the contents of a specific event dump. use when you have the dump identifier (from event-dump/create) and need its status and download urls. |
| `UNIONE_WEBHOOK_SET` | Set Webhook | Tool to set or edit a webhook event notification handler. use when you need to configure your webhook for event callbacks. |

## Supported Triggers

None listed.

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

The Unione MCP server is an implementation of the Model Context Protocol that connects your AI agent to Unione. It provides structured and secure access so your agent can perform Unione 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 Unione account and project
- Basic familiarity with async Python/Typescript

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

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 Unione 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, unione)
- 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 Unione 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=["unione"],
    )

    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 Unione actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Unione 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: ["unione"],
    },
  );

  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 Unione 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 Unione
```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 Unione, 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=["unione"],
    )

    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 Unione actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Unione 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: ["unione"],
    },
  );

  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 Unione 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 Unione to LlamaIndex through Composio's Tool Router MCP layer.
Key takeaways:
- Tool Router dynamically exposes Unione 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 Unione MCP Agent with another framework

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

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- [Ahrefs](https://composio.dev/toolkits/ahrefs) - Ahrefs is an SEO and marketing platform for site audits, keyword research, and competitor insights. It helps you improve search rankings and drive organic traffic.
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- [Cardly](https://composio.dev/toolkits/cardly) - Cardly is a platform for creating and sending personalized direct mail to customers. It helps businesses break through the digital clutter by getting real engagement via physical mailboxes.
- [ClickSend](https://composio.dev/toolkits/clicksend) - ClickSend is a cloud-based SMS and email marketing platform for businesses. It streamlines communication by enabling quick message delivery and contact management.
- [Crustdata](https://composio.dev/toolkits/crustdata) - CrustData is an AI-powered data intelligence platform for real-time company and people data. It helps B2B sales teams, AI SDRs, and investors react to live business signals.
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- [Cutt ly](https://composio.dev/toolkits/cutt_ly) - Cutt.ly is a URL shortening service for managing and analyzing links. Streamline your workflows with quick, trackable, and branded short URLs.
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## Frequently Asked Questions

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

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

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

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

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