# How to integrate Countdown api MCP with LlamaIndex

```json
{
  "title": "How to integrate Countdown api MCP with LlamaIndex",
  "toolkit": "Countdown api",
  "toolkit_slug": "countdown_api",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/countdown_api/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/countdown_api/framework/llama-index.md",
  "updated_at": "2026-05-12T10:07:41.168Z"
}
```

## Introduction

This guide walks you through connecting Countdown api to LlamaIndex using the Composio tool router. By the end, you'll have a working Countdown api agent that can list all your ebay data collections, start processing requests for a collection, get autocomplete suggestions for 'wireless earbuds' through natural language commands.
This guide will help you understand how to give your LlamaIndex agent real control over a Countdown api account through Composio's Countdown api MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Countdown api with

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

The Countdown api MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Countdown api account. It provides structured and secure access to real-time eBay marketplace data, so your agent can perform actions like searching eBay products, managing collections, retrieving seller feedback, and automating product data workflows on your behalf.
- eBay product search and autocomplete: Instantly fetch eBay autocomplete suggestions and help agents surface relevant product search terms and ideas in real time.
- Collection management and orchestration: Create, update, list, or delete collections to batch and organize multiple eBay data requests for streamlined marketplace analysis.
- Automated collection processing: Start or clear queued requests within a collection, making it easy to control and automate data gathering operations from eBay.
- Destination setup and notifications: Set up or remove destinations for results and notifications, ensuring your agent can manage where and how you receive processed eBay data.
- Access to rich eBay metadata: Retrieve detailed collection information, product details, customer reviews, and seller feedback to power analytics and business decisions.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `COUNTDOWN_API_CLEAR_ALL_REQUESTS` | Clear Collection Requests | Clears (removes) all pending requests from a collection. Use this to reset a collection before adding new requests, or to cancel all queued requests. The collection must be in 'idle' status. Returns the updated collection details after clearing. |
| `COUNTDOWN_API_COLLECTIONS_CREATE_COLLECTION` | Create a new collection | Tool to create a new collection. Use when you need to batch and orchestrate multiple requests on a schedule. |
| `COUNTDOWN_API_COLLECTIONS_GET_COLLECTION` | Get Collection | Tool to retrieve details for a single collection by ID. Use when you need collection metadata and counts for a given collection ID. |
| `COUNTDOWN_API_COLLECTIONS_LIST_COLLECTIONS` | List Collections | Tool to list all collections for the authenticated account. Use when you need to retrieve paginated collections with filtering, sorting, and timing constraints. |
| `COUNTDOWN_API_COLLECTIONS_START_COLLECTION` | Start Collection | Start processing a collection's queued requests on the Countdown API. Use this tool to manually trigger a collection to begin executing its requests. The collection must have at least one request added to it before it can be started. You need sufficient API credits to run all requests in the collection. Prerequisites: - Collection must exist (use List Collections or Create Collection first) - Collection must have at least one request added - Sufficient API credits available for the number of requests - Collection must not already be running |
| `COUNTDOWN_API_COLLECTIONS_UPDATE_COLLECTION` | Update an existing collection | Update an existing collection's settings. Use this to modify collection properties like name, schedule, priority, notification settings, or enabled status. The collection_id must be obtained from list_collections or create_collection. Only specified fields are updated; omitted fields retain their current values. |
| `COUNTDOWN_API_CORE_API_SEND_REQUEST` | eBay Autocomplete | Tool to fetch eBay autocomplete suggestions. Use when needing search-term-based suggestions from Countdown API. |
| `COUNTDOWN_API_CREATE_COLLECTION_REQUEST` | Create Collection Request | Tool to create new requests within a collection for bulk eBay data retrieval. Use when you need to add search, product, reviews, or other eBay data queries to a collection that will be executed on schedule. Collections must be in 'idle' status to accept new requests. |
| `COUNTDOWN_API_CREATE_DESTINATION` | Create Destination | Creates a cloud storage destination where batch result sets will be automatically uploaded. Supports Amazon S3, S3-compatible services (MinIO, DigitalOcean Spaces), Google Cloud Storage, Microsoft Azure Blob Storage, and Alibaba Cloud OSS. When enabled=true, the API validates credentials by uploading and deleting a test file. Set enabled=false to create the destination without credential validation. |
| `COUNTDOWN_API_DELETE_COLLECTION` | Delete Collection | Tool to delete a collection and its configuration by ID. Use when you need to remove an existing, non-running collection. |
| `COUNTDOWN_API_DELETE_DESTINATION` | Delete Destination | Tool to delete a destination by ID. Use when you need to remove an existing destination. |
| `COUNTDOWN_API_DELETE_SINGLE_REQUEST` | Delete Single Request | Delete a specific request from a Countdown API collection by its ID. Use this to remove individual requests that are no longer needed. The collection must not be running when deleting requests. |
| `COUNTDOWN_API_DESTINATIONS_LIST_DESTINATIONS` | List Destinations | Tool to list all destinations configured for the account. Use when you need to inspect or paginate through configured destinations. |
| `COUNTDOWN_API_FIND_COLLECTION_REQUESTS` | Find Collection Requests | Tool to find requests in a collection by custom_id or search query. Use when you need to search for specific requests within a collection using either an exact custom_id match or a text search query. |
| `COUNTDOWN_API_GET_ACCOUNT` | Get Account Information | Tool to retrieve account usage and current platform status. Use when needing to check plan, usage, and quota details for the authenticated user. |
| `COUNTDOWN_API_LIST_ERROR_LOGS` | List Error Logs | Tool to list error logs from collection executions. Returns recent errors encountered during request processing with details about failed requests and their causes. |
| `COUNTDOWN_API_REQUESTS_EXPORT_CSV` | Export Requests CSV | Export all requests from a collection as downloadable CSV files. Returns URLs to CSV files containing the request data. Use this tool when you need to bulk export or download collection request data in CSV format. The response includes paginated download links if the collection has many requests. Collections with no requests will return an empty pages array. |
| `COUNTDOWN_API_REQUESTS_EXPORT_JSON` | Export Requests as JSON | Tool to download all requests in a collection as JSON. Use when you need to export the entire request history for a collection. |
| `COUNTDOWN_API_REQUESTS_UPDATE_SINGLE_REQUEST` | Update Single Request | Tool to modify parameters of an existing request in a collection. Use when the collection is not running and you need to update eBay Product Data API parameters. |
| `COUNTDOWN_API_RESULTS_GET_RESULT_SET` | Get Result Set | Tool to retrieve a collection run's result set payload. Use after a collection run completes to fetch metadata and download links. |
| `COUNTDOWN_API_RESULTS_LIST_RESULT_SETS` | List Result Sets | Tool to list result sets produced by a collection. Use when you need to retrieve all summary status of result sets generated by a collection within the 14-day retention window. |
| `COUNTDOWN_API_RESULTS_RESEND_RESULT_SET_WEBHOOK` | Resend Result Set Webhook | Resend the webhook notification for a collection's result set. Use this tool to retry webhook delivery when the original webhook POST failed or timed out. The collection must have a notification_webhook URL configured (either on the collection itself or on the account profile). Result sets are only available for 14 days after creation. Prerequisites: - Collection must exist with a valid notification_webhook URL configured - Result set must exist and not be expired (14-day retention) - Use List Result Sets to find valid result_set_id values |
| `COUNTDOWN_API_STOP_ALL_COLLECTIONS` | Stop All Collections | Tool to stop all collections. Use when you need to halt any running or queued collections after reviewing operations. |
| `COUNTDOWN_API_STOP_COLLECTION` | Stop Collection | Tool to stop (pause) a single collection’s processing by ID. Use when you need to halt a running or queued collection after confirming the target collection ID. |
| `COUNTDOWN_API_UPDATE_DESTINATION` | Update Destination | Tool to update a destination's configuration by ID. Use after creating or retrieving a destination to modify its settings. |

## Supported Triggers

None listed.

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

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

### 1. Getting API Keys for OpenAI, Composio, and Countdown api

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 Countdown api 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, countdown api)
- 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 Countdown api 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=["countdown_api"],
    )

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

  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 Countdown api 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 Countdown api
```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 Countdown api, 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=["countdown_api"],
    )

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

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

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

## Related Toolkits

- [Addresszen](https://composio.dev/toolkits/addresszen) - Addresszen is a real-time address autocomplete and verification service. It helps capture accurate, deliverable addresses with instant suggestions and validation.
- [Asin data api](https://composio.dev/toolkits/asin_data_api) - Asin data api gives you detailed, real-time product data from Amazon, including price, rank, and reviews. Perfect for e-commerce pros and data-driven marketers who need instant marketplace insights.
- [Baselinker](https://composio.dev/toolkits/baselinker) - BaseLinker is an all-in-one e-commerce management platform connecting stores, marketplaces, carriers, and more. It streamlines order processing, inventory control, and automates your sales operations.
- [Bestbuy](https://composio.dev/toolkits/bestbuy) - Best Buy is a leading retailer offering APIs for product, store, and recommendation data. Instantly access up-to-date retail insights for smarter shopping and decision-making.
- [Btcpay server](https://composio.dev/toolkits/btcpay_server) - BTCPay Server is a free, open-source, self-hosted Bitcoin payment processor. It lets merchants accept Bitcoin payments directly, cutting out middlemen and boosting privacy.
- [Cdr platform](https://composio.dev/toolkits/cdr_platform) - Cdr platform is an API for purchasing carbon dioxide removal services. It enables businesses to offset emissions by accessing verified carbon removal projects.
- [Cloudcart](https://composio.dev/toolkits/cloudcart) - CloudCart is an e-commerce platform for building and managing online stores. It helps businesses streamline product listings, orders, and customer engagement.
- [Dpd2](https://composio.dev/toolkits/dpd2) - Dpd2 is a robust email management platform for handling, sorting, and automating email workflows. Streamline your communications and boost productivity with advanced sorting, labeling, and response tools.
- [Finerworks](https://composio.dev/toolkits/finerworks) - FinerWorks is an online platform for fine art and photo printing services. Artists and photographers use it to order custom prints and manage print inventory efficiently.
- [Fingertip](https://composio.dev/toolkits/fingertip) - Fingertip is a business management platform for selling, booking, and customer engagement—all from a single link. It helps businesses streamline operations and connect with customers across social channels.
- [Fraudlabs pro](https://composio.dev/toolkits/fraudlabs_pro) - FraudLabs Pro is an online payment fraud detection service for e-commerce and merchants. It helps minimize chargebacks and revenue loss by detecting and preventing fraudulent transactions.
- [Gift up](https://composio.dev/toolkits/gift_up) - Gift Up! is a digital platform for selling, managing, and redeeming gift cards online. It streamlines promotions and gift card transactions for businesses and their customers.
- [Goody](https://composio.dev/toolkits/goody) - Goody is a gifting platform that lets users send gifts and physical products without handling logistics. It streamlines gifting by managing delivery, fulfillment, and recipient experience.
- [Gumroad](https://composio.dev/toolkits/gumroad) - Gumroad is a platform for selling digital products, physical goods, and memberships with a simple checkout and marketing tools. It streamlines creator payouts and helps you grow your audience effortlessly.
- [Instacart](https://composio.dev/toolkits/instacart) - Instacart is an online grocery delivery and pickup service platform. It lets you discover local retailers and create shoppable lists and recipes with ease.
- [Junglescout](https://composio.dev/toolkits/junglescout) - Junglescout is an Amazon product research and analytics platform for sellers. It delivers sales estimates, competitive insights, and optimization tools to boost your Amazon business.
- [Ko fi](https://composio.dev/toolkits/ko_fi) - Ko-fi is a platform that lets creators receive donations, memberships, and sales from fans. It helps creators monetize their work and grow their audience with minimal friction.
- [Lemon squeezy](https://composio.dev/toolkits/lemon_squeezy) - Lemon Squeezy is a payments and subscription platform built for software companies. It makes managing payments, taxes, and customer subscriptions effortless.
- [Loyverse](https://composio.dev/toolkits/loyverse) - Loyverse is a point-of-sale (POS) platform for small businesses, offering tools for sales, inventory, and customer loyalty. It helps streamline retail operations and boost customer engagement.
- [Memberstack](https://composio.dev/toolkits/memberstack) - Memberstack lets you add user authentication, payments, and member management to your website—no backend code required. Easily manage your site's members and subscriptions from a single platform.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Countdown api MCP?

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

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

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

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