# How to integrate Renderform MCP with Pydantic AI ```json { "title": "How to integrate Renderform MCP with Pydantic AI", "toolkit": "Renderform", "toolkit_slug": "renderform", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/renderform/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/renderform/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:23:47.425Z" } ``` ## Introduction This guide walks you through connecting Renderform to Pydantic AI using the Composio tool router. By the end, you'll have a working Renderform agent that can generate a personalized event invitation image, create a product promo banner with logo, make a qr code image for a website through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Renderform account through Composio's Renderform MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Renderform with - [OpenAI Agents SDK](https://composio.dev/toolkits/renderform/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/renderform/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/renderform/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/renderform/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/renderform/framework/codex) - [OpenClaw](https://composio.dev/toolkits/renderform/framework/openclaw) - [Hermes](https://composio.dev/toolkits/renderform/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/renderform/framework/cli) - [Google ADK](https://composio.dev/toolkits/renderform/framework/google-adk) - [LangChain](https://composio.dev/toolkits/renderform/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/renderform/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/renderform/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/renderform/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/renderform/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 Renderform - 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 Renderform 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 Renderform MCP server, and what's possible with it? The Renderform MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Renderform account. It provides structured and secure access to your Renderform templates and image creation features, so your agent can generate customized images, update templates, add dynamic content, and automate design workflows on your behalf. - Dynamic image generation: Automatically create images based on templates with custom text, images, colors, or QR codes filled in by your agent. - Template management and population: Let your agent select and update design templates, swapping in new content or branding elements as needed. - Automated content personalization: Enable your agent to personalize graphics for marketing, events, or customer outreach by programmatically inserting unique details for each recipient. - Batch rendering and bulk design: Have your agent generate multiple images in one go for campaigns, product catalogs, or social posts, saving time and reducing manual work. - Seamless integration with other workflows: Connect image creation to broader automation flows, letting your agent trigger Renderform actions as part of end-to-end processes. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `RENDERFORM_CREATE_PDF_FROM_URL` | Create PDF from URL | Tool to create a PDF from any website URL with customizable page format and auto-expiration. Use when you need to generate a PDF snapshot of a webpage. | | `RENDERFORM_DELETE_RESULT` | Delete Result | Tool to delete a render result (image/PDF) by its identifier. Use when you need to remove a previously generated image or PDF from the system. | | `RENDERFORM_GET_RESULT` | Get Result | Tool to retrieve detailed information about a specific render result by its identifier. Use when you need to check the status or get the full details of a previously generated image or PDF. | | `RENDERFORM_LIST_FONTS` | List Fonts | Tool to retrieve all available fonts including Google Fonts and custom uploaded fonts. Use when you need to list fonts for text rendering. | | `RENDERFORM_LIST_RESULTS_V2` | List Results (v2) | Tool to list all rendered images and PDFs with pagination support. Use when retrieving results from the v2 API with filtering by template or batch. | | `RENDERFORM_LIST_TEMPLATES_V2` | List Templates V2 | Tool to get a list of all your templates with metadata and preview links. Supports pagination and filtering by name and tags. | | `RENDERFORM_TAKE_SCREENSHOT` | Take Website Screenshot | Tool to capture a screenshot of a website with given dimensions. Use when you need a visual snapshot of a page with optional wait time before capture. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Renderform MCP server is an implementation of the Model Context Protocol that connects your AI agent to Renderform. It provides structured and secure access so your agent can perform Renderform 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 Renderform - 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 Renderform - MCPServerStreamableHTTP connects to the Renderform 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 Renderform 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 Renderform composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["renderform"], ) 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 Renderform endpoint - The agent uses GPT-5 to interpret user commands and perform Renderform operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent renderform_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[renderform_mcp], instructions=( "You are a Renderform assistant. Use Renderform 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 - Renderform 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 Renderform.\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 Renderform composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["renderform"], ) 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 renderform_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[renderform_mcp], instructions=( "You are a Renderform assistant. Use Renderform 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 Renderform.\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 Renderform through Composio's Tool Router. With this setup, your agent can perform real Renderform 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 + Renderform 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 Renderform MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/renderform/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/renderform/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/renderform/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/renderform/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/renderform/framework/codex) - [OpenClaw](https://composio.dev/toolkits/renderform/framework/openclaw) - [Hermes](https://composio.dev/toolkits/renderform/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/renderform/framework/cli) - [Google ADK](https://composio.dev/toolkits/renderform/framework/google-adk) - [LangChain](https://composio.dev/toolkits/renderform/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/renderform/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/renderform/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/renderform/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/renderform/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 Renderform MCP? With a standalone Renderform MCP server, the agents and LLMs can only access a fixed set of Renderform tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Renderform 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 Renderform tools. ### Can I manage the permissions and scopes for Renderform while using Tool Router? Yes, absolutely. You can configure which Renderform 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 Renderform data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)