# How to integrate Vestaboard MCP with Pydantic AI

```json
{
  "title": "How to integrate Vestaboard MCP with Pydantic AI",
  "toolkit": "Vestaboard",
  "toolkit_slug": "vestaboard",
  "framework": "Pydantic AI",
  "framework_slug": "pydantic-ai",
  "url": "https://composio.dev/toolkits/vestaboard/framework/pydantic-ai",
  "markdown_url": "https://composio.dev/toolkits/vestaboard/framework/pydantic-ai.md",
  "updated_at": "2026-05-06T08:33:13.117Z"
}
```

## Introduction

This guide walks you through connecting Vestaboard to Pydantic AI using the Composio tool router. By the end, you'll have a working Vestaboard agent that can list all your vestaboard subscriptions, send 'lunch is ready!' to kitchen board, display daily quote on office vestaboard through natural language commands.
This guide will help you understand how to give your Pydantic AI agent real control over a Vestaboard account through Composio's Vestaboard MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Vestaboard with

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

## 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 Vestaboard
- 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 Vestaboard 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 Vestaboard MCP server, and what's possible with it?

The Vestaboard MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Vestaboard account. It provides structured and secure access to your Vestaboard displays, so your agent can perform actions like listing subscriptions, sending custom messages, and managing display content on your behalf.
- List all Vestaboard subscriptions: Easily retrieve and review every Vestaboard subscription linked to your account so you know exactly which displays your agent can manage.
- Send messages to any board: Direct your agent to post custom messages or notifications to a specific Vestaboard subscription in real time, perfect for reminders, daily schedules, or inspirational quotes.
- Automate display updates: Schedule or trigger automatic content updates across multiple Vestaboards without manual effort, keeping your displays fresh and relevant.
- Centralized display management: Manage all your connected Vestaboards from one place—no need to hop between apps or devices to update your displays.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `VESTABOARD_LIST_SUBSCRIPTIONS` | List Subscriptions | Tool to list all subscriptions accessible to the authenticated user. use when you need to retrieve your subscriptions. example: "list my subscriptions on vestaboard." |
| `VESTABOARD_SUBSCRIPTION_API_SEND_MESSAGE` | Send Message to Subscription | Tool to send a message to a specific vestaboard subscription. use after confirming you have the subscription id. example: "send 'hello!' to subscription sub ab12cd34ef." |

## Supported Triggers

None listed.

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

The Vestaboard MCP server is an implementation of the Model Context Protocol that connects your AI agent to Vestaboard. It provides structured and secure access so your agent can perform Vestaboard 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 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

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

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

Install the required libraries.
What's happening:
- composio connects your agent to external SaaS tools like Vestaboard
- pydantic-ai lets you create structured AI agents with tool support
- python-dotenv loads your environment variables securely from a .env file
```bash
pip install composio pydantic-ai python-dotenv
```

### 3. Set up environment variables

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
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key
```

### 4. Import dependencies

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

### 5. Create a Tool Router Session

What's happening:
- We're creating a Tool Router session that gives your agent access to Vestaboard 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
```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 Vestaboard
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["vestaboard"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
```

### 6. Initialize the Pydantic AI Agent

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

### 7. Build the chat interface

What's happening:
- The agent reads input from the terminal and streams its response
- Vestaboard API calls happen automatically under the hood
- The model keeps conversation history to maintain context across turns
```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 Vestaboard.\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()
```

### 8. Run the application

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

## Complete Code

```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 Vestaboard
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["vestaboard"],
    )
    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
    vestaboard_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[vestaboard_mcp],
        instructions=(
            "You are a Vestaboard assistant. Use Vestaboard 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 Vestaboard.\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 Vestaboard through Composio's Tool Router. With this setup, your agent can perform real Vestaboard 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 + Vestaboard for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.

## How to build Vestaboard MCP Agent with another framework

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

## Related Toolkits

- [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.
- [Outlook](https://composio.dev/toolkits/outlook) - Outlook is Microsoft's email and calendaring platform for unified communications and scheduling. It helps users stay organized with powerful email, contacts, and calendar management.
- [Slack](https://composio.dev/toolkits/slack) - Slack is a channel-based messaging platform for teams and organizations. It helps people collaborate in real time, share files, and connect all their tools in one place.
- [Gong](https://composio.dev/toolkits/gong) - Gong is a platform for video meetings, call recording, and team collaboration. It helps teams capture conversations, analyze calls, and turn insights into action.
- [Microsoft teams](https://composio.dev/toolkits/microsoft_teams) - Microsoft Teams is a collaboration platform that combines chat, meetings, and file sharing within Microsoft 365. It keeps distributed teams connected and productive through seamless virtual communication.
- [Slackbot](https://composio.dev/toolkits/slackbot) - Slackbot is a conversational automation tool for Slack that handles reminders, notifications, and automated responses. It boosts team productivity by streamlining onboarding, answering FAQs, and managing timely alerts—all right inside Slack.
- [2chat](https://composio.dev/toolkits/_2chat) - 2chat is an API platform for WhatsApp and multichannel text messaging. It streamlines chat automation, group management, and real-time messaging for developers.
- [Agent mail](https://composio.dev/toolkits/agent_mail) - Agent mail provides AI agents with dedicated email inboxes for sending, receiving, and managing emails. It empowers agents to communicate autonomously with people, services, and other agents—no human intervention needed.
- [Basecamp](https://composio.dev/toolkits/basecamp) - Basecamp is a project management and team collaboration tool by 37signals. It helps teams organize tasks, share files, and communicate efficiently in one place.
- [Chatwork](https://composio.dev/toolkits/chatwork) - Chatwork is a team communication platform with group chats, file sharing, and task management. It helps businesses boost collaboration and streamline productivity.
- [Clickmeeting](https://composio.dev/toolkits/clickmeeting) - ClickMeeting is a cloud-based platform for running online meetings and webinars. It helps businesses and individuals host, manage, and engage virtual audiences with ease.
- [Confluence](https://composio.dev/toolkits/confluence) - Confluence is Atlassian's team collaboration and knowledge management platform. It helps your team organize, share, and update documents and project content in one secure workspace.
- [Dailybot](https://composio.dev/toolkits/dailybot) - DailyBot streamlines team collaboration with chat-based standups, reminders, and polls. It keeps work flowing smoothly in your favorite messaging platforms.
- [Dialmycalls](https://composio.dev/toolkits/dialmycalls) - Dialmycalls is a mass notification service for sending voice and text messages to contacts. It helps teams and organizations quickly broadcast urgent alerts and updates.
- [Dialpad](https://composio.dev/toolkits/dialpad) - Dialpad is a cloud-based business phone and contact center system for teams. It unifies voice, video, messaging, and meetings across your devices.
- [Discord](https://composio.dev/toolkits/discord) - Discord is a real-time messaging and VoIP platform for communities and teams. It lets users chat, share media, and collaborate across public and private channels.
- [Discordbot](https://composio.dev/toolkits/discordbot) - Discordbot is an automation tool for Discord servers that handles moderation, messaging, and user engagement. It helps communities run smoothly by automating routine and complex tasks.
- [Echtpost](https://composio.dev/toolkits/echtpost) - Echtpost is a secure digital communication platform for encrypted document and message exchange. It ensures confidential data stays private and protected during transmission.
- [Egnyte](https://composio.dev/toolkits/egnyte) - Egnyte is a cloud-based platform for secure file sharing, storage, and governance. It helps teams collaborate efficiently while maintaining data compliance and security.
- [Google Meet](https://composio.dev/toolkits/googlemeet) - Google Meet is a secure video conferencing platform for virtual meetings, chat, and screen sharing. It helps teams connect, collaborate, and communicate seamlessly from anywhere.

## Frequently Asked Questions

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

With a standalone Vestaboard MCP server, the agents and LLMs can only access a fixed set of Vestaboard tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Vestaboard and many other apps based on the task at hand, all through a single MCP endpoint.

### Can I use Tool Router MCP with Pydantic AI?

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 Vestaboard tools.

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

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

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