# How to integrate Bart MCP with LlamaIndex

```json
{
  "title": "How to integrate Bart MCP with LlamaIndex",
  "toolkit": "Bart",
  "toolkit_slug": "bart",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/bart/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/bart/framework/llama-index.md",
  "updated_at": "2026-05-12T10:02:26.975Z"
}
```

## Introduction

This guide walks you through connecting Bart to LlamaIndex using the Composio tool router. By the end, you'll have a working Bart agent that can find next departures from embarcadero station, get real-time trip updates for richmond line, check current bart service advisories and alerts through natural language commands.
This guide will help you understand how to give your LlamaIndex agent real control over a Bart account through Composio's Bart MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Bart with

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

The Bart MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to BART's public transit data. It provides structured and secure access to real-time schedules, route information, station details, and service advisories, so your agent can plan trips, fetch live updates, check advisories, and explore routes for you.
- Trip planning with live schedules: Instantly retrieve train arrival or departure times and help users plan journeys between any BART stations based on the latest schedule data.
- Live service advisories and alerts: Keep travelers informed by fetching up-to-date system-wide or station-specific service advisories, ensuring users know about delays or disruptions before they travel.
- Route and station discovery: Access detailed information about BART routes and stations, including amenities and configuration, so your agent can answer travel questions or recommend stations.
- Real-time trip and schedule updates: Get the latest trip updates and schedule changes in real time, allowing users to adapt plans quickly if there are changes or issues along their route.
- Access to static and GTFS feeds: Download the latest BART GTFS (General Transit Feed Specification) data for offline schedule planning, analysis, or integration with third-party transit tools.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `BART_BART_GET_API_VERSION` | Get BART API Version | Get the current version of the BART API. This action retrieves version information for the BART (Bay Area Rapid Transit) API, including the current API version number, copyright information, and license details. This is useful for verifying API compatibility and ensuring you're working with the expected API version. The BART API is currently at version 3.10 and supports both XML and JSON output formats. Use this action to confirm which version of the API you're interfacing with and to access licensing information. |
| `BART_GET_ELEVATOR_STATUS` | Get Elevator Status | Tool to fetch current elevator status across all BART stations. Use when you need real-time elevator availability information for accessibility planning or route guidance. |
| `BART_GET_ESTIMATED_DEPARTURES` | Get Estimated Departures | Tool to get real-time estimated departure times for a specified BART station. Returns live train departure predictions including minutes until departure, platform assignments, train lengths, line colors, bicycle accommodation, and delay information. Use this when you need current departure times for planning trips or checking train status. |
| `BART_GET_FARE` | Get BART Fare | Get fare information between two BART stations including Clipper and cash prices. Returns multiple fare types (Clipper, cash, senior/disabled, youth, Clipper START) with their respective prices. Use this when you need to find out how much a BART trip costs between two stations. |
| `BART_GET_GTFS_ALERTS` | Get GTFS-RT Service Alerts | Tool to fetch GTFS-RT service alerts in protobuf format for integration with GTFS static feed. Use when you need real-time service advisories, disruptions, or alert information. |
| `BART_GET_GTFS_RT_TRIP_UPDATES` | Get GTFS-RT Trip Updates | Tool to fetch real-time trip updates in GTFS-Realtime format. Use when you need the latest live trip information as raw protobuf. |
| `BART_GET_GTFS_STATIC_SCHEDULE_FEED` | Download GTFS Static Schedule Feed | Downloads the BART static GTFS (General Transit Feed Specification) schedule feed as a ZIP archive. The GTFS feed contains comprehensive transit data including stations, routes, trip schedules, fares, and service calendars in standardized CSV format. Use this to access complete BART schedule information for route planning, analysis, or integration with transit applications. |
| `BART_BART_GET_ROUTE_INFO` | Get Route Info | Tool to fetch detailed information about a specific BART route. Use when you know the route number (1–12) or need all routes configuration. Call after confirming the route ID. |
| `BART_GET_ROUTE_SCHEDULE` | Get Route Schedule | Tool to get detailed schedule information for a specific BART route showing all trains and their stops. Use when you need to see the complete schedule for a route including departure times, station stops, bike policies, and passenger load indicators. Call this after determining the specific route number (1-12). |
| `BART_GET_SCHEDULE_ARRIVE` | Get BART Schedule Arrive | Tool to retrieve schedule information based on a specified arrival time. Use when planning trips arriving by a given time. |
| `BART_GET_SCHEDULE_DEPART` | Get BART Schedule Depart | Get BART train schedules departing from an origin station to a destination station at a specified time. Returns multiple trip options with departure/arrival times, fares (Clipper, cash, senior/disabled, youth), transfer details, train information, and platform numbers. Use this when you need to plan BART trips with specific departure times or when users ask about train schedules between two stations. |
| `BART_GET_SERVICE_ADVISORIES` | Get Service Advisories | Tool to fetch current BART service advisories. Use when you need up-to-date system-wide or station-level alerts before presenting or planning transit routes. |
| `BART_GET_STATION_ACCESS` | Get Station Access | Get comprehensive station access information including parking, transit, bike facilities, and lockers. Returns detailed access information for a specific BART station including: entering/exiting instructions, parking availability and lot capacity, bike parking and bike station details, locker availability, car-sharing options, nearby destinations, and connected transit services. Use this when you need to help users understand how to access a BART station or what facilities are available. |
| `BART_GET_STATION_INFO` | Get Station Info | Get detailed information for a specific BART station by its abbreviation code. Returns comprehensive station details including: name, location (address, city, county, coordinates), routes serving the station (northbound/southbound), platform information, nearby amenities (food, shopping, attractions), and general station description. Use this when you need detailed information about a specific BART station and you already have its 4-letter abbreviation code (e.g., 'EMBR' for Embarcadero, 'MONT' for Montgomery Street, '12TH' for 12th Street Oakland). |
| `BART_BART_GET_STATIONS` | Get BART Stations | Get a list of all BART stations with their complete information. This action retrieves information about all BART (Bay Area Rapid Transit) stations including station names, abbreviation codes, geographic coordinates (latitude/longitude), and full addresses. This is useful for finding station locations, getting station codes for other API calls, or building station lookup tools. |
| `BART_GET_STATION_SCHEDULE` | Get Station Schedule | Get detailed scheduled departure information for a specific BART station. Returns all trains departing from the station including route line, destination, departure time, bike allowance, crowding level, and platform number. Use this when you need to see all departures from a specific station. |
| `BART_GET_TRAIN_COUNT` | Get Train Count | Tool to fetch current count of trains active in the BART system. Use when you need real-time information about how many trains are currently operating. |
| `BART_LIST_ROUTES` | List BART Routes | Tool to get a list of all current BART routes/lines with basic information. Use when you need to see all available routes, their colors, directions, or route numbers. |

## Supported Triggers

None listed.

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

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

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

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 Bart 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, bart)
- 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 Bart 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=["bart"],
    )

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

  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 Bart 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 Bart
```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 Bart, 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=["bart"],
    )

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

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

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

## Related Toolkits

- [Google Calendar](https://composio.dev/toolkits/googlecalendar) - Google Calendar is a time management service for scheduling meetings, events, and reminders. It streamlines personal and team organization with integrated notifications and sharing options.
- [Apaleo](https://composio.dev/toolkits/apaleo) - Apaleo is a cloud-based property management platform for hospitality businesses. It centralizes reservations, billing, and daily operations for smoother hotel management.
- [Appointo](https://composio.dev/toolkits/appointo) - Appointo is an appointment booking platform for Shopify stores. It lets businesses add online scheduling to their websites with zero coding.
- [Bookingmood](https://composio.dev/toolkits/bookingmood) - Bookingmood is commission-free booking software for rental businesses. It lets you manage reservations and sync bookings directly on your website.
- [Booqable](https://composio.dev/toolkits/booqable) - Booqable is a rental software platform for managing inventory, bookings, and reservations. It helps businesses streamline rentals and keep track of every item with ease.
- [Cal](https://composio.dev/toolkits/cal) - Cal is a meeting scheduling platform that offers shareable booking links and real-time calendar syncing. It streamlines the process of finding mutual availability to make scheduling effortless.
- [Calendarhero](https://composio.dev/toolkits/calendarhero) - Calendarhero is a powerful scheduling platform that streamlines your calendar management across multiple services. It helps you efficiently schedule, reschedule, and organize meetings without the back-and-forth.
- [Calendly](https://composio.dev/toolkits/calendly) - Calendly is an appointment scheduling tool that automates meeting invitations, availability checks, and reminders. It helps individuals and teams avoid endless email back-and-forth when booking meetings.
- [Etermin](https://composio.dev/toolkits/etermin) - eTermin is an online appointment scheduling platform for businesses to manage bookings. It streamlines client appointments, saving time and reducing scheduling conflicts.
- [Evenium](https://composio.dev/toolkits/evenium) - Evenium is an all-in-one platform for managing professional events, from planning to analysis. It helps teams simplify event logistics, boost engagement, and track every detail in one place.
- [Eventee](https://composio.dev/toolkits/eventee) - Eventee is a user-friendly event management platform for mobile and web. It boosts attendee engagement for in-person, virtual, and hybrid events.
- [Eventzilla](https://composio.dev/toolkits/eventzilla) - Eventzilla is an event management platform for creating, promoting, and running events. It streamlines ticketing, registration, and attendee coordination for organizers.
- [Humanitix](https://composio.dev/toolkits/humanitix) - Humanitix is a not-for-profit ticketing platform that donates 100% of profits to charity. It empowers event organizers to make social impact with every ticket sold.
- [Lodgify](https://composio.dev/toolkits/lodgify) - Lodgify is an all-in-one vacation rental software for property managers and owners. It centralizes bookings, guest messaging, and channel synchronization in one dashboard.
- [Planyo Online Booking](https://composio.dev/toolkits/planyo_online_booking) - Planyo Online Booking is a flexible reservation system for managing bookings by day, hour, or event. It streamlines scheduling for any business needing reservations.
- [Scheduleonce](https://composio.dev/toolkits/scheduleonce) - Scheduleonce is a scheduling platform for capturing, qualifying, and engaging with inbound leads. It streamlines appointment booking and follow-ups for faster lead conversion.
- [Supersaas](https://composio.dev/toolkits/supersaas) - Supersaas is a flexible appointment scheduling platform for businesses and individuals. It streamlines bookings, reminders, and calendar management in one place.
- [Sympla](https://composio.dev/toolkits/sympla) - Sympla is a platform for managing in-person and online events, ticket sales, and registrations. It streamlines event setup, attendee tracking, and digital content delivery.
- [Gmail](https://composio.dev/toolkits/gmail) - Gmail is Google's email service with powerful spam protection, search, and G Suite integration. It keeps your inbox organized and makes communication fast and reliable.
- [Google Drive](https://composio.dev/toolkits/googledrive) - Google Drive is a cloud storage platform for uploading, sharing, and collaborating on files. It's perfect for keeping your documents accessible and organized across devices.

## Frequently Asked Questions

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

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

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

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

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