# How to integrate Thanks io MCP with LlamaIndex

```json
{
  "title": "How to integrate Thanks io MCP with LlamaIndex",
  "toolkit": "Thanks io",
  "toolkit_slug": "thanks_io",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/thanks_io/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/thanks_io/framework/llama-index.md",
  "updated_at": "2026-05-12T10:28:21.213Z"
}
```

## Introduction

This guide walks you through connecting Thanks io to LlamaIndex using the Composio tool router. By the end, you'll have a working Thanks io agent that can add new customer to holiday mailing list, show all available handwritten font styles, create a mailing list for event attendees through natural language commands.
This guide will help you understand how to give your LlamaIndex agent real control over a Thanks io account through Composio's Thanks io MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Thanks io with

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

The Thanks io MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Thanks io account. It provides structured and secure access to your direct mail platform, so your agent can perform actions like managing mailing lists, sending personalized postcards, choosing templates, and handling recipients automatically on your behalf.
- Mailing list management: Effortlessly create, list, or delete mailing lists, and keep your recipient groups organized for targeted campaigns.
- Recipient automation: Quickly add or remove recipients from mailing lists, ensuring your contacts are always up to date and ready for new mailings.
- Personalized mail creation: Enable your agent to select from available handwriting styles or image templates, so every postcard, letter, or notecard feels truly unique.
- Template selection and preview: Browse and choose from message and image templates to customize your direct mail content for any occasion.
- Automated sending workflows: Trigger stored send actions to deliver mailings at the right moment, keeping your outreach timely and efficient.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `THANKS_IO_ADD_RECIPIENT_TO_MAILING_LIST` | Add Recipient to Mailing List | Tool to add a new recipient to a mailing list. Use after confirming recipient and list IDs. |
| `THANKS_IO_CREATE_MAILING_LIST` | Create Mailing List | Tool to create a new mailing list. Use when you need to group contacts under a fresh list before adding recipients. |
| `THANKS_IO_DELETE_MAILING_LIST` | Delete Mailing List | Tool to delete a mailing list. Use when you need to remove an entire mailing list by its ID. Confirm the list ID before calling. Example: "Delete the mailing list with ID 123e4567-e89b-12d3-a456-426614174000". |
| `THANKS_IO_DELETE_RECIPIENT_FROM_MAILING_LIST` | Delete Recipient from Mailing List | Tool to remove a recipient from a mailing list. Use after confirming the recipient's ID. |
| `THANKS_IO_DELETE_SUB_ACCOUNT` | Delete Sub-Account | Tool to delete a specific sub-account by ID. Use when you need to remove an existing sub-account. Confirm the ID before calling. |
| `THANKS_IO_EXECUTE_STORED_SEND` | Execute Stored Send | Tool to execute a previously created stored send. Use after creating a stored send to trigger delivery. The response body is empty; success is indicated by a 200 or 204 status. |
| `THANKS_IO_LIST_HANDWRITING_STYLES` | List Handwriting Styles | Tool to retrieve available handwriting styles. Use when selecting a style for handwritten personalization. |
| `THANKS_IO_LIST_IMAGE_TEMPLATES` | List Image Templates | Tool to retrieve a list of available image templates. Use when you need to browse or select a template for mailings. |
| `THANKS_IO_LIST_MAILING_LISTS` | List Mailing Lists | Tool to list all mailing lists. Use when you need to fetch existing lists before managing recipients. |
| `THANKS_IO_LIST_MESSAGE_TEMPLATES` | List Message Templates | Tool to list available message templates. Use when selecting a template for a mailing. |
| `THANKS_IO_MAILING_LISTS_BUY_RADIUS_SEARCH` | Buy Radius Search Mailing List | Tool to buy or append a radius search mailing list based on address and radius. Use when you need targeted mailing lists around a specified address. |
| `THANKS_IO_ORDER_PREVIEW_LETTER` | Preview letter send | Tool to preview a letter send as PDF. Use when you need to confirm letter content before placing the final order. Returns PDF preview URLs. |
| `THANKS_IO_ORDER_PREVIEW_NOTECARD` | Preview Notecard | Tool to preview a notecard send. Use when you need front and back images before placing an actual notecard order. |
| `THANKS_IO_ORDER_PREVIEW_WINDOWLESS_LETTER` | Preview Windowless Letter | Tool to preview a windowless letter send. Use when you need a PDF preview of the cover-only letter before placing an order. |
| `THANKS_IO_ORDERS_LIST` | List Orders | Tool to list recent orders. Use after placing orders to fetch the latest history, optionally filtering by sub-account or limiting the result count. |
| `THANKS_IO_ORDERS_SEARCH_BY_ADDRESS` | Search Orders by Recipient Street Address | Tool to search orders by recipient street address. Use when you need to find all orders sent to a specific street address. |
| `THANKS_IO_RECIPIENTS_CREATE_MULTI` | Create Multiple Recipients | Tool to create multiple recipients at once in a mailing list. Use when batching recipient additions for efficiency. |
| `THANKS_IO_RECIPIENTS_DELETE_BY_ADDRESS` | Delete Recipient by Address | Tool to delete a recipient by address and postal code. Use when you need to remove a recipient without their ID. |
| `THANKS_IO_RECIPIENTS_GET_DETAILS` | Get Recipient Details | Tool to get details for a specific recipient by ID. Use to verify a recipient’s full address and custom fields. |
| `THANKS_IO_RECIPIENTS_SEARCH_BY_EMAIL` | Search Recipients by Email | Tool to search recipients by email across mailing lists. Use when you need to find all recipients matching an email in specific lists. Example: "Find recipients with email test@test.com in lists [1,2,3]." |
| `THANKS_IO_RECIPIENTS_UPDATE` | Update Recipient | Tool to update existing recipient details by recipient ID. Use when modifying recipient data after confirming the recipient exists. |
| `THANKS_IO_SEND_POSTCARD` | Send Postcard | Tool to send a customized postcard. Use when you need to dispatch a physical postcard with a chosen image and handwritten message. |
| `THANKS_IO_STORED_SEND_NOTECARD` | Stored Send Notecard | Tool to create a stored send for a notecard. Use when you need to schedule mailing of a personalized notecard at a later time after preparing payload. |
| `THANKS_IO_STORED_SEND_POSTCARD` | Stored Send Postcard | Tool to create a stored send for a postcard. Use when you need to prepare and schedule postcard orders for later execution; returns a URL to finalize and send. |
| `THANKS_IO_STORED_SEND_WINDOWLESS_LETTER` | Stored Send Windowless Letter | Tool to create a stored send for a windowless letter. Use when you need to prepare a letter order for later execution. |
| `THANKS_IO_SUB_ACCOUNTS_CREATE` | Create Sub-Account | Tool to create a new sub-account. Use when you need to manage separate profiles with distinct return addresses and settings. |
| `THANKS_IO_SUB_ACCOUNTS_LIST` | List Sub Accounts | Tool to list all available sub-accounts. Use when you need to select a sub-account for operations requiring a sub-account context. |
| `THANKS_IO_SUB_ACCOUNTS_SHOW` | Get Sub Account Details | Tool to retrieve details for a specific sub-account by ID. Use when you need full configuration of a sub-account before performing sub-account scoped operations. |
| `THANKS_IO_SUB_ACCOUNTS_UPDATE` | Update Sub-Account | Tool to update details for a specific sub-account. Use when modifying title or return address details of a sub-account. Confirm sub-account ID before calling. |

## Supported Triggers

None listed.

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

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

### 1. Getting API Keys for OpenAI, Composio, and Thanks io

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 Thanks io 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, thanks io)
- 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 Thanks io 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=["thanks_io"],
    )

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

  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 Thanks io 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 Thanks io
```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 Thanks io, 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=["thanks_io"],
    )

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

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

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

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

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

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

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