# How to integrate Zeplin MCP with Pydantic AI ```json { "title": "How to integrate Zeplin MCP with Pydantic AI", "toolkit": "Zeplin", "toolkit_slug": "zeplin", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/zeplin/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/zeplin/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:31:12.652Z" } ``` ## 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 list all project styleguides in zeplin, get all screens for a specific project, fetch comments from a specific zeplin screen 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. ## Also integrate Zeplin with - [OpenAI Agents SDK](https://composio.dev/toolkits/zeplin/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/zeplin/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/zeplin/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/zeplin/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/zeplin/framework/codex) - [OpenClaw](https://composio.dev/toolkits/zeplin/framework/openclaw) - [Hermes](https://composio.dev/toolkits/zeplin/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/zeplin/framework/cli) - [Google ADK](https://composio.dev/toolkits/zeplin/framework/google-adk) - [LangChain](https://composio.dev/toolkits/zeplin/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/zeplin/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/zeplin/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/zeplin/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/zeplin/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 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. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `ZEPLIN_AUTH_O_AUTH_AUTHORIZE` | Start OAuth authorization (PKCE) | Tool to start OAuth 2.0 authorization code flow for Zeplin apps. Use when initiating user authorization; call before exchanging the code. | | `ZEPLIN_CONNECTED_COMPONENTS_PROJECT_LIST` | List Project Connected Components | Tool to list connected components in a Zeplin project. Use when you have the project_id and need to retrieve connected components in a specific project. | | `ZEPLIN_PROJECT_COLORS_LIST` | List Project Colors | Tool to list colors in a Zeplin project. Use when you need to fetch defined color tokens at the project level after obtaining the project ID. | | `ZEPLIN_PROJECT_COLOR_UPDATE` | Update Project Color | Tool to update a color in a Zeplin project. Use when you need to modify RGBA channels or source ID of an existing color after confirming the project and color IDs. | | `ZEPLIN_PROJECTS_GET` | Get Zeplin Project by ID | Tool to get a Zeplin project by ID. Use when you need detailed info about a specific project after confirming its project_id. | | `ZEPLIN_PROJECTS_MEMBERS_INVITE` | Invite Project Member | Tool to invite a user to a Zeplin project. Use when you need to add a member by email or username to a project after obtaining the project ID. | | `ZEPLIN_PROJECT_TEXT_STYLES_LIST` | List Project Text Styles | Tool to list text styles in a Zeplin project. Use when you need to fetch typography tokens defined at the project level after obtaining the project ID. | | `ZEPLIN_PROJECT_TEXT_STYLE_UPDATE` | Update Project Text Style | Tool to update a text style in a Zeplin project. Use when you need to modify typography settings of an existing text style after confirming the project and text style IDs. | | `ZEPLIN_SCREEN_ANNOTATION_DELETE` | Delete Screen Annotation | Tool to delete a screen annotation in Zeplin. Use when you need to remove a specific annotation from a Zeplin screen given its IDs. | | `ZEPLIN_SCREEN_ANNOTATION_GET` | Get Screen Annotation | Tool to fetch a single screen annotation. Use when you know the screen_id and annotation_id and need the detailed annotation data. | | `ZEPLIN_SCREEN_ANNOTATIONS_LIST` | List Screen Annotations | Tool to list annotations for a Zeplin screen. Use when you have the screen_id and need its annotations. | | `ZEPLIN_SCREEN_ANNOTATIONS_UPDATE` | Update Screen Annotation | Tool to update a screen annotation's content, position, or type. Use after confirming screen_id and annotation_id. | | `ZEPLIN_SCREEN_COMPONENTS_LIST` | List Screen Components | Tool to list components in a Zeplin screen. Use when you have the screen_id and need to retrieve UI components in a specific screen. | | `ZEPLIN_SCREEN_SECTION_GET` | Get Screen Section | Tool to get a single screen section. Use when you need to fetch detailed information of a screen section by its ID after confirming the project and section IDs. | | `ZEPLIN_SCREEN_SECTIONS_LIST` | List Screen Sections | Tool to list screen sections in a Zeplin project. Use when you need a paginated list of screen sections after confirming the project ID. | | `ZEPLIN_SCREEN_VERSION_GET` | Get Screen Version | Tool to retrieve a specific screen version. Use after specifying both screen_id and version_id when you need detailed snapshot metadata. | | `ZEPLIN_SCREEN_VERSIONS_CREATE` | Create Screen Version | Tool to create a new version of a screen. Use when uploading a new design snapshot (PNG or JPEG image) as a screen version. Requires a valid project_id, screen_id, and an image file. Optionally include a commit_message. | | `ZEPLIN_SCREEN_VERSIONS_LIST` | List Screen Versions | Tool to list all versions of a screen. Use when you need to enumerate past screen snapshots after obtaining a screen ID. | | `ZEPLIN_STYLEGUIDE_COLOR_CREATE` | Create Styleguide Color | Tool to create a new styleguide color. Use after obtaining the styleguide ID to define custom color tokens. | | `ZEPLIN_STYLEGUIDE_COLORS_LIST` | List Styleguide Colors | Tool to list colors in a Zeplin styleguide. Use when you need to fetch defined color tokens after obtaining the styleguide ID. | | `ZEPLIN_STYLEGUIDE_COLOR_UPDATE` | Update Styleguide Color | Tool to update a color in a Zeplin styleguide. Use after obtaining the styleguide and color IDs. | | `ZEPLIN_STYLEGUIDE_TEXT_STYLES_LIST` | List Styleguide Text Styles | Tool to list text styles in a Zeplin styleguide. Use when you need to fetch defined typography tokens after obtaining the styleguide ID. | | `ZEPLIN_STYLEGUIDE_TEXT_STYLE_UPDATE` | Update Styleguide Text Style | Tool to update a text style in a Zeplin styleguide. Use when you need to modify typography settings of an existing text style after confirming the styleguide and text style IDs. | | `ZEPLIN_USERS_GET_PERSONAL_PROJECTS` | List Personal Projects | Tool to list personal projects. Use when you need to fetch all projects in the current user's personal workspace after authentication. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Zeplin MCP server is an implementation of the Model Context Protocol that connects your AI agent to Zeplin. It provides structured and secure access so your agent can perform Zeplin 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 Zeplin - 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 Zeplin - MCPServerStreamableHTTP connects to the Zeplin 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 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 ```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") ``` ### 6. Initialize the Pydantic AI Agent 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 ```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." ), ) ``` ### 7. Build the chat interface 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 ```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() ``` ### 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 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. ## How to build Zeplin MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/zeplin/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/zeplin/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/zeplin/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/zeplin/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/zeplin/framework/codex) - [OpenClaw](https://composio.dev/toolkits/zeplin/framework/openclaw) - [Hermes](https://composio.dev/toolkits/zeplin/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/zeplin/framework/cli) - [Google ADK](https://composio.dev/toolkits/zeplin/framework/google-adk) - [LangChain](https://composio.dev/toolkits/zeplin/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/zeplin/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/zeplin/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/zeplin/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/zeplin/framework/crew-ai) ## Related Toolkits - [Figma](https://composio.dev/toolkits/figma) - Figma is a collaborative interface design tool for teams and individuals. It streamlines design workflows with real-time collaboration and easy sharing. - [Abyssale](https://composio.dev/toolkits/abyssale) - Abyssale is a creative automation platform for generating images, videos, GIFs, PDFs, and HTML5 content programmatically. It streamlines and scales visual content production for marketing, design, and operations teams. - [Alttext ai](https://composio.dev/toolkits/alttext_ai) - AltText.ai is a service that generates alt text for images automatically. It helps boost accessibility and SEO for your visual content. - [Bannerbear](https://composio.dev/toolkits/bannerbear) - Bannerbear is an API-driven platform for generating images and videos automatically at scale. It helps businesses create custom graphics, social visuals, and marketing assets using powerful templates. - [Canva](https://composio.dev/toolkits/canva) - Canva is a drag-and-drop design suite for creating professional graphics, presentations, and marketing materials. It makes it easy for anyone to design with beautiful templates and a vast library of elements. - [Claid ai](https://composio.dev/toolkits/claid_ai) - Claid.ai delivers AI-driven image editing APIs for tasks like background removal, upscaling, and color correction. It helps automate and enhance image workflows with powerful, developer-friendly tools. - [Cloudinary](https://composio.dev/toolkits/cloudinary) - Cloudinary is a cloud-based platform for managing, uploading, and transforming images and videos. It streamlines media workflows and delivers optimized assets globally. - [Cults](https://composio.dev/toolkits/cults) - Cults is a digital marketplace for 3D printing models, connecting designers and makers. It lets creators share, sell, and discover a huge variety of printable designs easily. - [DeepImage](https://composio.dev/toolkits/deepimage) - DeepImage is an AI-powered image enhancer and upscaler. Get higher-quality images with just a few clicks. - [Dreamstudio](https://composio.dev/toolkits/dreamstudio) - DreamStudio is Stability AI’s platform for generating and editing images with AI. It lets you easily turn ideas into stunning visuals, fast. - [Dynapictures](https://composio.dev/toolkits/dynapictures) - Dynapictures is a cloud-based platform for generating personalized images at scale. Instantly create hundreds of custom visuals using your data sources, like Google Sheets. - [Fal.ai](https://composio.dev/toolkits/fal_ai) - Fal.ai is a generative media platform offering 600+ AI models for images, video, voice, and audio. Developers use Fal.ai for fast, scalable access to cutting-edge generative AI tools. - [Gamma](https://composio.dev/toolkits/gamma) - Gamma is an AI-powered platform for making beautiful, interactive presentations and documents. It lets anyone create and share engaging decks with minimal effort. - [Html to image](https://composio.dev/toolkits/html_to_image) - Html to image converts HTML and CSS into images or captures web page screenshots. Instantly generate visuals from code or web content—no manual screenshots needed. - [Imagior](https://composio.dev/toolkits/imagior) - Imagior is an AI-powered image generation platform that lets you create and customize images using dynamic templates and APIs. Perfect for businesses and creators needing fast, scalable visuals without design hassle. - [Imejis io](https://composio.dev/toolkits/imejis_io) - Imejis io is an API-based image generation platform with powerful customization and template support. It lets you create and modify images in seconds, no manual design work required. - [Imgix](https://composio.dev/toolkits/imgix) - Imgix is a real-time image processing and delivery service for developers. It helps you optimize, transform, and deliver images efficiently at any scale. - [Kraken io](https://composio.dev/toolkits/kraken_io) - Kraken.io is an image optimization and compression platform. It helps you shrink image file sizes while keeping visual quality intact. - [Logo dev](https://composio.dev/toolkits/logo_dev) - Logo.dev is an API and database for high-resolution company logos and brand metadata. Instantly fetch official logos from any domain without scraping or manual searching. - [Miro](https://composio.dev/toolkits/miro) - Miro is a collaborative online whiteboard platform for teams to brainstorm, design, and manage projects visually. It streamlines teamwork by enabling real-time idea sharing, diagramming, and workflow planning in a single space. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Zeplin MCP? With a standalone Zeplin MCP server, the agents and LLMs can only access a fixed set of Zeplin tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Zeplin 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 Zeplin tools. ### Can I manage the permissions and scopes for Zeplin while using Tool Router? Yes, absolutely. You can configure which Zeplin 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 Zeplin data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)