# How to integrate Backendless MCP with LlamaIndex

```json
{
  "title": "How to integrate Backendless MCP with LlamaIndex",
  "toolkit": "Backendless",
  "toolkit_slug": "backendless",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/backendless/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/backendless/framework/llama-index.md",
  "updated_at": "2026-05-12T10:02:19.950Z"
}
```

## Introduction

This guide walks you through connecting Backendless to LlamaIndex using the Composio tool router. By the end, you'll have a working Backendless agent that can list all files in the user uploads folder, create a new directory for project assets, retrieve users where status is active through natural language commands.
This guide will help you understand how to give your LlamaIndex agent real control over a Backendless account through Composio's Backendless MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Backendless with

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

The Backendless MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Backendless account. It provides structured and secure access to your backend services, so your agent can perform actions like managing file storage, retrieving and updating database records, handling directories, and orchestrating server-side logic on your behalf.
- Dynamic file and directory management: Allow your agent to create, copy, delete, and list files or folders in your Backendless storage, keeping your app data organized.
- Database record retrieval and filtering: Empower the agent to fetch objects from specific tables with advanced filtering, sorting, and pagination for instant data access.
- Automated backend task scheduling: Let the agent create or delete timers to run recurring or one-off server-side logic, enabling powerful backend automation.
- Custom Hive resource management: Instruct your agent to create new Backendless Hive resources and retrieve full maps of stored values for scalable, flexible data handling.
- Safe data cleanup: Make it easy for your agent to remove obsolete files, directories, or scheduled tasks, helping maintain a tidy and efficient backend environment.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `BACKENDLESS_COPY_FILE` | Copy File | Tool to copy a file or directory within Backendless file storage. Use when duplicating files to a new location after verifying source and destination paths. |
| `BACKENDLESS_CREATE_DIRECTORY` | Create Directory | Tool to create a new directory at the specified path. Use when you need to organize files under a new folder structure. |
| `BACKENDLESS_CREATE_HIVE` | Create Backendless Hive | Tool to create a new Hive. Use when you need to provision a new Hive resource before performing Hive operations. Example: Create a hive named 'groceryStore'. |
| `BACKENDLESS_CREATE_TIMER` | Create Backendless Timer | Tool to create a new timer with schedule and code. Use when scheduling recurring or one-off tasks to run server-side logic after confirming parameters. |
| `BACKENDLESS_DELETE_DIRECTORY` | Delete Directory | Tool to delete a directory at the specified path in Backendless file storage. Use when you need to remove folders after confirming the path. |
| `BACKENDLESS_DELETE_FILE` | Delete File | Deletes a file from Backendless file storage at the specified path. Use this tool when you need to remove files from storage. The operation is permanent and cannot be undone. Ensure the file path is correct before deletion. |
| `BACKENDLESS_DELETE_TIMER` | Delete Backendless Timer | Deletes a Backendless timer by its unique name. Use this tool to permanently remove a scheduled timer from your Backendless application. The timer must exist and you must provide its exact name. Once deleted, the timer's scheduled executions will stop immediately and cannot be recovered. Note: Requires access to Backendless Console Management API (available with Plus or Enterprise plans). |
| `BACKENDLESS_DIRECTORY_LISTING` | Directory Listing | Tool to retrieve a listing of files and directories at a given path. Use when browsing or filtering file storage directories. |
| `BACKENDLESS_GENERAL_OBJECT_RETRIEVAL` | General Object Retrieval | Tool to retrieve objects from a specified Backendless table with filtering, sorting, and pagination. Use after confirming the table name and query options. Example: "Get Users where age > 30 sorted by created desc". |
| `BACKENDLESS_GET_ALL_VALUES` | Get All Values | Tool to retrieve all values from a map in a specified Hive. Use when you need to fetch the entire contents of a Hive map at once. |
| `BACKENDLESS_GET_COUNTER_VALUE` | Get Counter Value | Tool to retrieve the current value of a Backendless counter. Use when you need to inspect an atomic counter's value. |
| `BACKENDLESS_GET_FILE_COUNT` | Get File Count | Tool to get the count of files in a Backendless directory. Use when you need to determine how many items match a filter or include subdirectories. |
| `BACKENDLESS_GET_KEY_ITEMS` | Get Key Items | Tool to retrieve values for a specified key in a list (all, single, or range). Use when you need specific elements or the entire list from a Hive key. Supports single index retrieval, range retrieval, or full list. |
| `BACKENDLESS_GET_TIMER` | Get Backendless Timer | Tool to retrieve information about a specific timer. Use when you need to inspect a timer's schedule and next run details by name. |
| `BACKENDLESS_MAP_PUT` | Map Put | Tool to set or update key-value pairs in a Hive map. Use when you need to add or update multiple entries in a Hive map. |
| `BACKENDLESS_MOVE_FILE` | Move File | Tool to move a file or directory within Backendless file storage. Use when relocating resources to a new path after verifying source and destination. |
| `BACKENDLESS_PUBLISH_MESSAGE` | Publish Message | Tool to publish a message to a specified messaging channel. Use when you need to send notifications or events to subscribers after confirming channel and payload. |
| `BACKENDLESS_RESET_COUNTER` | Reset Counter | Tool to reset a Backendless counter back to zero. Use when you need to reinitialize a counter before starting a new sequence. |
| `BACKENDLESS_SET_COUNTER_VALUE` | Set Counter Value | Tool to set a Backendless counter to a specific value conditionally. Use when you need to ensure the counter only updates if it currently matches an expected value. |
| `BACKENDLESS_UPDATE_TIMER` | Update Backendless Timer | Tool to update schedule or code of an existing timer. Use when you need to modify a timer's configuration after retrieval. |
| `BACKENDLESS_USER_DELETE` | Delete User | Tool to delete a user by user ID. Use when removing a user account after confirming permissions. |
| `BACKENDLESS_USER_FIND` | Find User by ID | Tool to retrieve user information by ID. Use when you need to fetch details for a specific user after you have their objectId. |
| `BACKENDLESS_USER_GRANT_PERMISSION` | Grant Permission to User | Tool to grant a permission to a user on a specific data object. Use when precise access rights must be assigned after verifying the table and object IDs. Example: "Grant FIND permission to a user for a Person record". |
| `BACKENDLESS_USER_LOGIN` | User Login | Tool to log in a registered user with identity and password. Use when you need to authenticate a user before making subsequent requests. Example: "Login alice@wonderland.com with password wonderland". |
| `BACKENDLESS_USER_LOGOUT` | User Logout | Tool to log out the currently authenticated user. Use when you need to terminate the user session after operations. |
| `BACKENDLESS_USER_PASSWORD_RECOVERY` | User Password Recovery | Tool to initiate password recovery for a user. Use when a user requests a password reset after forgetting their password. Triggers an email with recovery instructions. |
| `BACKENDLESS_USER_REGISTRATION` | User Registration | Tool to register a new user with email and password. Use when creating a user account or converting a guest account to a registered one after collecting credentials. Example: Register 'alice@wonderland.com' with password 'wonderland'. |
| `BACKENDLESS_USER_REVOKE_PERMISSION` | Revoke Permission from User | Tool to revoke a permission from a specified user or role on a specific data object. Use when you need to deny a previously granted operation for a user or role on a data object after verifying the table and object IDs. |
| `BACKENDLESS_USER_UPDATE` | Update User | Tool to update properties of an existing Backendless user. Use when you need to modify user profile fields after login. Example: Update phoneNumber to "5551212". |
| `BACKENDLESS_VALIDATE_USER_TOKEN` | Validate User Token | Tool to validate a user session token. Use after obtaining a token from login to confirm the session is active. |

## Supported Triggers

None listed.

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

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

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

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 Backendless 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, backendless)
- 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 Backendless 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=["backendless"],
    )

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

  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 Backendless 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 Backendless
```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 Backendless, 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=["backendless"],
    )

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

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

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

## Related Toolkits

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- [Codeinterpreter](https://composio.dev/toolkits/codeinterpreter) - Codeinterpreter is a Python-based coding environment with built-in data analysis and visualization. It lets you instantly run scripts, plot results, and prototype solutions inside supported platforms.
- [GitHub](https://composio.dev/toolkits/github) - GitHub is a code hosting platform for version control and collaborative software development. It streamlines project management, code review, and team workflows in one place.
- [Ably](https://composio.dev/toolkits/ably) - Ably is a real-time messaging platform for live chat and data sync in modern apps. It offers global scale and rock-solid reliability for seamless, instant experiences.
- [Abuselpdb](https://composio.dev/toolkits/abuselpdb) - Abuselpdb is a central database for reporting and checking IPs linked to malicious online activity. Use it to quickly identify and report suspicious or abusive IP addresses.
- [Alchemy](https://composio.dev/toolkits/alchemy) - Alchemy is a blockchain development platform offering APIs and tools for Ethereum apps. It simplifies building and scaling Web3 projects with robust infrastructure.
- [Algolia](https://composio.dev/toolkits/algolia) - Algolia is a hosted search API that powers lightning-fast, relevant search experiences for web and mobile apps. It helps developers deliver instant, typo-tolerant, and scalable search without complex infrastructure.
- [Anchor browser](https://composio.dev/toolkits/anchor_browser) - Anchor browser is a developer platform for AI-powered web automation. It transforms complex browser actions into easy API endpoints for streamlined web interaction.
- [Apiflash](https://composio.dev/toolkits/apiflash) - Apiflash is a website screenshot API for programmatically capturing web pages. It delivers high-quality screenshots on demand for automation, monitoring, or reporting.
- [Apiverve](https://composio.dev/toolkits/apiverve) - Apiverve delivers a suite of powerful APIs that simplify integration for developers. It's designed for reliability and scalability so you can build faster, smarter applications without the integration headache.
- [Appcircle](https://composio.dev/toolkits/appcircle) - Appcircle is an enterprise-grade mobile CI/CD platform for building, testing, and publishing mobile apps. It streamlines mobile DevOps so teams ship faster and with more confidence.
- [Appdrag](https://composio.dev/toolkits/appdrag) - Appdrag is a cloud platform for building websites, APIs, and databases with drag-and-drop tools and code editing. It accelerates development and iteration by combining hosting, database management, and low-code features in one place.
- [Appveyor](https://composio.dev/toolkits/appveyor) - AppVeyor is a cloud-based continuous integration service for building, testing, and deploying applications. It helps developers automate and streamline their software delivery pipelines.
- [Baserow](https://composio.dev/toolkits/baserow) - Baserow is an open-source no-code database platform for building collaborative data apps. It makes it easy for teams to organize data and automate workflows without writing code.
- [Bench](https://composio.dev/toolkits/bench) - Bench is a benchmarking tool for automated performance measurement and analysis. It helps you quickly evaluate, compare, and track your systems or workflows.
- [Better stack](https://composio.dev/toolkits/better_stack) - Better Stack is a monitoring, logging, and incident management solution for apps and services. It helps teams ensure application reliability and performance with real-time insights.
- [Bitbucket](https://composio.dev/toolkits/bitbucket) - Bitbucket is a Git-based code hosting and collaboration platform for teams. It enables secure repository management and streamlined code reviews.
- [Blazemeter](https://composio.dev/toolkits/blazemeter) - Blazemeter is a continuous testing platform for web and mobile app performance. It empowers teams to automate and analyze large-scale tests with ease.
- [Blocknative](https://composio.dev/toolkits/blocknative) - Blocknative delivers real-time mempool monitoring and transaction management for public blockchains. Instantly track pending transactions and optimize blockchain interactions with live data.
- [Bolt iot](https://composio.dev/toolkits/bolt_iot) - Bolt IoT is a platform for building and managing IoT projects with cloud-based device control and monitoring. It makes connecting sensors and actuators to the internet seamless for automation and data insights.

## Frequently Asked Questions

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

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

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

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

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