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How to integrate Zeplin MCP with Pydantic AI

Connect Pydantic AI to Zeplin MCP. List all project styleguides in zeplin, get all screens for a specific project, and more using natural language, with authentication handled for you.

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Zeplin is a collaborative workspace for designers and developers to organize and hand off design projects. It streamlines design file sharing and communication for smoother product development.

24 Tools

Introduction

This guide walks you through connecting Zeplin to Pydantic AI using the Composio tool router. By the end, you'll have a working Zeplin agent that can browse projects and screens, review screen annotations, manage styleguide colors and text styles, and invite project members through natural language commands.

This guide will help you understand how to give your Pydantic AI agent real control over a Zeplin account through Composio's Zeplin MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

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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 Zeplin
  • 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 Zeplin 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 Zeplin MCP server, and what's possible with it?

The Zeplin MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Zeplin account. It provides structured and secure access to your Zeplin workspace, so your agent can perform actions like listing projects, fetching screens, exporting assets, managing components, and collaborating with your design team on your behalf.

  • Project and styleguide management: Let your agent list, fetch, or organize your Zeplin projects and associated styleguides for faster design handoff and reference.
  • Screen and asset retrieval: Automatically pull screen details, preview images, or export assets from any project directly into your workflow, no copy-paste required.
  • Component library access: Have your agent fetch, list, or update components from your shared libraries to keep your design system in sync.
  • Commenting and collaboration: Enable your agent to read, create, or manage comments on screens or components, streamlining feedback and design review cycles.
  • Resource linking and metadata extraction: Allow your agent to extract, organize, or provide direct links to design resources and metadata, making documentation and developer handoff seamless.

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 Zeplin
  • 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 Zeplin
  • MCPServerStreamableHTTP connects to the Zeplin 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 Zeplin
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["zeplin"],
    )
    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 Zeplin 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
zeplin_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[zeplin_mcp],
    instructions=(
        "You are a Zeplin assistant. Use Zeplin tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Zeplin endpoint
  • The agent uses GPT-5 to interpret user commands and perform Zeplin 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 Zeplin.\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
  • Zeplin 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 Zeplin 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 Zeplin
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["zeplin"],
    )
    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
    zeplin_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[zeplin_mcp],
        instructions=(
            "You are a Zeplin assistant. Use Zeplin 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 Zeplin.\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 Zeplin through Composio's Tool Router. With this setup, your agent can perform real Zeplin 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 + Zeplin 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 Zeplin action and event your agent gets out of the box.

Start OAuth authorization (PKCE)

Construct an OAuth 2.

List Project Connected Components

List connected components in a Zeplin project by project ID.

List Project Colors

List colors defined in a Zeplin project by project ID, for example to retrieve its color tokens.

Update Project Color

Tool to update a color in a Zeplin project.

Get Zeplin Project by ID

Get a Zeplin project by ID to view details about a specific project.

Invite Project Member

Invite a user to a Zeplin project by handle, with an optional role.

List Project Text Styles

List text styles in a Zeplin project by project ID to retrieve its typography tokens.

Update Project Text Style

Update an existing text style in a Zeplin project by project and text style IDs.

Delete Screen Annotation

Tool to delete a screen annotation in Zeplin.

Get Screen Annotation

Tool to fetch a single screen annotation.

List Screen Annotations

List a page of annotations for a Zeplin screen by screen ID.

Update Screen Annotation

Update a screen annotation's content, position, or type by screen and annotation IDs.

List Screen Components

List components in a Zeplin screen by screen ID.

Get Screen Section

Tool to get a single screen section.

List Screen Sections

List screen sections in a Zeplin project by project ID.

Get Screen Version

Retrieve a specific Zeplin screen version by screen and version IDs to inspect its snapshot metadata.

Create Screen Version

Tool to create a new version of a screen.

List Screen Versions

List a page of versions for a Zeplin screen to enumerate its past snapshots.

Create Styleguide Color

Create a color token in a Zeplin styleguide.

List Styleguide Colors

List color tokens in a Zeplin styleguide by styleguide ID.

Update Styleguide Color

Update a color in a Zeplin styleguide.

List Styleguide Text Styles

List a page of text styles in a Zeplin styleguide to retrieve its typography tokens.

Update Styleguide Text Style

Update typography settings of a text style in a Zeplin styleguide.

List Personal Projects

List a page of projects in the current user's personal workspace.

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FAQ

Frequently asked questions

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