How to integrate Bart MCP with Pydantic AI

This guide walks you through connecting Bart to Pydantic AI 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 Pydantic AI 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.

Get started for freeGet a demo
Bart logoBart
Api Key

Bart is the Bay Area Rapid Transit system, providing fast public transportation across the San Francisco Bay Area. It helps commuters and travelers get real-time schedule info, plan routes, and stay updated on service changes.

18 Tools

Introduction

This guide walks you through connecting Bart to Pydantic AI 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 Pydantic AI 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 SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Bart
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Bart workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed 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.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active API key
  • Basic familiarity with Python and async programming
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Bart
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs
5

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Bart
  • MCPServerStreamableHTTP connects to the Bart MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Bart
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["bart"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Bart tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
bart_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[bart_mcp],
    instructions=(
        "You are a Bart assistant. Use Bart tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Bart endpoint
  • The agent uses GPT-5 to interpret user commands and perform Bart operations
  • The instructions field defines the agent's role and behavior
8

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Bart.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Bart API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Bart and Pydantic AI:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Bart
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["bart"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    bart_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[bart_mcp],
        instructions=(
            "You are a Bart assistant. Use Bart tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Bart.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've built a Pydantic AI agent that can interact with Bart through Composio's Tool Router. With this setup, your agent can perform real Bart actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Bart for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
TOOLS

Supported Tools

Every Bart action and event your agent gets out of the box.

Get BART API Version

Get the current version of the BART API.

Get Elevator Status

Tool to fetch current elevator status across all BART stations.

Get Estimated Departures

Tool to get real-time estimated departure times for a specified BART station.

Get BART Fare

Get fare information between two BART stations including Clipper and cash prices.

Get GTFS-RT Service Alerts

Tool to fetch GTFS-RT service alerts in protobuf format for integration with GTFS static feed.

Get GTFS-RT Trip Updates

Tool to fetch real-time trip updates in GTFS-Realtime format.

Download GTFS Static Schedule Feed

Downloads the BART static GTFS (General Transit Feed Specification) schedule feed as a ZIP archive.

Get Route Info

Tool to fetch detailed information about a specific BART route.

Get Route Schedule

Tool to get detailed schedule information for a specific BART route showing all trains and their stops.

Get BART Schedule Arrive

Tool to retrieve schedule information based on a specified arrival time.

Get BART Schedule Depart

Get BART train schedules departing from an origin station to a destination station at a specified time.

Get Service Advisories

Tool to fetch current BART service advisories.

Get Station Access

Get comprehensive station access information including parking, transit, bike facilities, and lockers.

Get Station Info

Get detailed information for a specific BART station by its abbreviation code.

Get BART Stations

Get a list of all BART stations with their complete information.

Get Station Schedule

Get detailed scheduled departure information for a specific BART station.

Get Train Count

Tool to fetch current count of trains active in the BART system.

List BART Routes

Tool to get a list of all current BART routes/lines with basic information.

FRAMEWORKS

How to build Bart MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Bart MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Bart MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Bart MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Bart MCP with Claude Cowork

Codex logo

Codex

Use Bart MCP with Codex

OpenClaw logo

OpenClaw

Use Bart MCP with OpenClaw

Hermes logo

Hermes

Use Bart MCP with Hermes

CLI logo

CLI

Use Bart MCP with CLI

Google ADK logo

Google ADK

Use Bart MCP with Google ADK

LangChain logo

LangChain

Use Bart MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Bart MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Bart MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Bart MCP with LlamaIndex

CrewAI logo

CrewAI

Use Bart MCP with CrewAI

FAQ

Frequently asked questions

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.

Yes, you can. Pydantic AI 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.

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.

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.

Start with Bart.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Bart tool your agent needs.Free to start.

Start building
Customer stories

Teams ship real agents on Composio

GET STARTED FOR FREEBOOK A DEMO
agent.aiZoomLettaGleanHubSpotAWSWabi

Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati
Harsha Gaddipati
Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya
Abhi Arya
Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave
Nirman Dave
CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu
Ryan Yu
Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

Tomisin Jenrola
Tomisin Jenrola
Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Jerome Leclanche
Jerome Leclanche
Co-Founder, Ingram Technologies