# How to integrate Cloudlayer MCP with Pydantic AI ```json { "title": "How to integrate Cloudlayer MCP with Pydantic AI", "toolkit": "Cloudlayer", "toolkit_slug": "cloudlayer", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/cloudlayer/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/cloudlayer/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:06:50.160Z" } ``` ## Introduction This guide walks you through connecting Cloudlayer to Pydantic AI using the Composio tool router. By the end, you'll have a working Cloudlayer agent that can generate pdf from a contract html template, convert a marketing webpage to a png image, list your most recent generated assets through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Cloudlayer account through Composio's Cloudlayer MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Cloudlayer with - [OpenAI Agents SDK](https://composio.dev/toolkits/cloudlayer/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/cloudlayer/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/cloudlayer/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/cloudlayer/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/cloudlayer/framework/codex) - [OpenClaw](https://composio.dev/toolkits/cloudlayer/framework/openclaw) - [Hermes](https://composio.dev/toolkits/cloudlayer/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/cloudlayer/framework/cli) - [Google ADK](https://composio.dev/toolkits/cloudlayer/framework/google-adk) - [LangChain](https://composio.dev/toolkits/cloudlayer/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/cloudlayer/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/cloudlayer/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/cloudlayer/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/cloudlayer/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 Cloudlayer - 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 Cloudlayer 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 Cloudlayer MCP server, and what's possible with it? The Cloudlayer MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Cloudlayer account. It provides structured and secure access to dynamic document and asset generation, so your agent can perform actions like converting HTML or URLs to PDFs or images, managing assets, and configuring storage on your behalf. - Automated PDF and image generation: Instantly convert HTML content or public URLs into professional PDFs and images for reporting, documentation, or sharing. - Asset management and retrieval: Let your agent fetch metadata or download links for generated assets, or list your most recent document and image creations. - Dynamic storage configuration: Seamlessly add and manage external storage buckets or containers for organizing generated files and assets. - Real-time API health monitoring: Enable your agent to check Cloudlayer API status, ensuring your integrations are always up and running. - Flexible screenshot and rendering tasks: Capture dynamic webpage screenshots as images or PDFs, with full control over conversion parameters, for advanced automation workflows. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `CLOUDLAYER_ADD_STORAGE` | Add Storage | Add a user-owned S3-compatible storage configuration for storing generated assets. This action allows Enterprise plan users to configure their own S3-compatible storage (AWS S3, DigitalOcean Spaces, Wasabi, MinIO, etc.) instead of using the built-in cloud storage included with Cloudlayer accounts. Note: User storage is only available on Enterprise plans. Standard plans will receive an 'allowed: false' response indicating the feature requires a plan upgrade. | | `CLOUDLAYER_CONVERT_HTML_TO_IMAGE` | Convert HTML to Image (V2) | Convert HTML content to an image (PNG, JPG, or WebP) using the v2 API endpoint. Renders the provided HTML string using a headless browser and returns job details with the generated image asset. Supports various rendering options including viewport configuration, transparency, auto-scroll, and custom wait conditions. | | `CLOUDLAYER_CONVERT_HTML_TO_PDF_V2` | Convert HTML to PDF (v2) | Tool to convert HTML content to PDF using CloudLayer v2 API. Use when you need to generate a PDF from raw HTML with advanced options like custom paper size, margins, headers/footers, and viewport settings. The HTML is automatically Base64 encoded before sending to the API. | | `CLOUDLAYER_CONVERT_URL_TO_PDF_GET` | Convert URL to PDF (Simple) | Tool to convert a URL to PDF using GET request. Use when you need quick PDF conversion with minimal parameters and immediate result. | | `CLOUDLAYER_DELETE_STORAGE` | Delete Storage Configuration | Tool to delete a specific user storage configuration. Use when you need to remove an external bucket configuration by its ID after confirming the ID is correct. | | `CLOUDLAYER_GET_ACCOUNT_INFO` | Get Account Info | Tool to retrieve Cloudlayer account usage, credits, and document counts. Use when monitoring account limits and subscription status. | | `CLOUDLAYER_GET_ASSET` | Get Asset | Tool to retrieve a specific asset by its ID. Use when you need to fetch metadata or download URL of an existing asset after its generation. | | `CLOUDLAYER_GET_JOB_BY_ID` | Get Job By ID | Retrieve details of a specific Cloudlayer job by its ID. Use this to check the status of an async job, get the asset download URL after completion, or view job parameters. Returns 401 if the job ID doesn't exist or doesn't belong to your account. | | `CLOUDLAYER_GET_STATUS` | Get API Status | Tool to test API reachability. Use when checking if the Cloudlayer API is available. | | `CLOUDLAYER_GET_STORAGE_BY_ID` | Get Storage Configuration by ID | Tool to retrieve a specific storage configuration by its ID. Use when you need to inspect or validate details of a user storage configuration. | | `CLOUDLAYER_LIST_ASSETS` | List Assets | List assets in your CloudLayer account with cursor-based pagination. Returns PDFs and images generated via HTML/URL conversion jobs. Use this to find asset IDs for further operations like downloading or deleting assets. | | `CLOUDLAYER_LIST_JOBS` | List Jobs | List jobs in your CloudLayer account with cursor-based pagination. Use when you need to view your recent jobs and their statuses. | | `CLOUDLAYER_LIST_STORAGE` | List Storage Configurations | Retrieves all user storage configurations (S3-compatible buckets) for the authenticated Cloudlayer account. Use this to view configured external storage destinations where generated documents can be saved. Note: User Storage is an Enterprise plan feature. Non-Enterprise accounts will receive an empty list. | | `CLOUDLAYER_TEMPLATE_TO_PDF` | Template to PDF | Generate a PDF document from an HTML/Nunjucks template with dynamic data. Provide either: - A `templateId` for predefined templates from CloudLayer's template library, OR - A base64-encoded `template` string containing custom HTML/Nunjucks markup. The `data` parameter populates template variables (e.g., {{name}}, {{items}}) with your JSON data. By default, jobs run asynchronously and return a job ID to poll for completion via get_job_by_id. | | `CLOUDLAYER_URL_TO_IMAGE_POST` | Convert URL to Image | Converts a webpage URL to an image (PNG, JPG, or WebP). Supports custom viewport settings, wait conditions, transparency, auto-scroll, and thumbnail preview generation. The API is asynchronous - use the returned job ID to poll for results. | | `CLOUDLAYER_URL_TO_PDF_POST` | Convert URL to PDF | Tool to convert a URL to PDF with full parameter support. Use when you need advanced control over paper size, margins, headers/footers, or webhook callbacks. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Cloudlayer MCP server is an implementation of the Model Context Protocol that connects your AI agent to Cloudlayer. It provides structured and secure access so your agent can perform Cloudlayer 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 Cloudlayer - 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 Cloudlayer - MCPServerStreamableHTTP connects to the Cloudlayer 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 Cloudlayer 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 Cloudlayer composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["cloudlayer"], ) 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 Cloudlayer endpoint - The agent uses GPT-5 to interpret user commands and perform Cloudlayer operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent cloudlayer_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[cloudlayer_mcp], instructions=( "You are a Cloudlayer assistant. Use Cloudlayer 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 - Cloudlayer 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 Cloudlayer.\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 Cloudlayer composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["cloudlayer"], ) 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 cloudlayer_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[cloudlayer_mcp], instructions=( "You are a Cloudlayer assistant. Use Cloudlayer 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 Cloudlayer.\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 Cloudlayer through Composio's Tool Router. With this setup, your agent can perform real Cloudlayer 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 + Cloudlayer 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 Cloudlayer MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/cloudlayer/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/cloudlayer/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/cloudlayer/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/cloudlayer/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/cloudlayer/framework/codex) - [OpenClaw](https://composio.dev/toolkits/cloudlayer/framework/openclaw) - [Hermes](https://composio.dev/toolkits/cloudlayer/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/cloudlayer/framework/cli) - [Google ADK](https://composio.dev/toolkits/cloudlayer/framework/google-adk) - [LangChain](https://composio.dev/toolkits/cloudlayer/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/cloudlayer/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/cloudlayer/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/cloudlayer/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/cloudlayer/framework/crew-ai) ## Related Toolkits - [Google Drive](https://composio.dev/toolkits/googledrive) - Google Drive is a cloud storage platform for uploading, sharing, and collaborating on files. It's perfect for keeping your documents accessible and organized across devices. - [Google Docs](https://composio.dev/toolkits/googledocs) - Google Docs is a cloud-based word processor that enables document creation and real-time collaboration. Its seamless sharing and version history make team editing and content management a breeze. - [Google Super](https://composio.dev/toolkits/googlesuper) - Google Super is an all-in-one suite combining Gmail, Drive, Calendar, Sheets, Analytics, and more. It gives you a unified platform to manage your digital life, boosting productivity and organization. - [Affinda](https://composio.dev/toolkits/affinda) - Affinda is an AI-powered document processing platform that automates data extraction from resumes, invoices, and more. It streamlines document-heavy workflows by turning files into structured, actionable data. - [Agility cms](https://composio.dev/toolkits/agility_cms) - Agility CMS is a headless content management system for building and managing digital experiences across platforms. It lets teams update content quickly and deliver omnichannel experiences with ease. - [Algodocs](https://composio.dev/toolkits/algodocs) - Algodocs is an AI-powered platform that automates data extraction from business documents. It delivers fast, secure, and accurate processing without templates or manual training. - [Api2pdf](https://composio.dev/toolkits/api2pdf) - Api2Pdf is a REST API for generating PDFs from HTML, URLs, and documents using powerful engines like wkhtmltopdf and Headless Chrome. It streamlines document conversion and automation for developers and businesses. - [Aryn](https://composio.dev/toolkits/aryn) - Aryn is an AI-powered platform for parsing, extracting, and analyzing data from unstructured documents. Use it to automate document processing and unlock actionable insights from your files. - [Boldsign](https://composio.dev/toolkits/boldsign) - Boldsign is a digital eSignature platform for sending, signing, and tracking documents online. Organizations use it to automate agreements and manage legally binding workflows efficiently. - [Boloforms](https://composio.dev/toolkits/boloforms) - BoloForms is an eSignature platform built for small businesses, offering unlimited signatures, templates, and forms. It simplifies digital document signing and team collaboration at a predictable, fixed price. - [Box](https://composio.dev/toolkits/box) - Box is a cloud content management and file sharing platform for businesses. It helps teams securely store, organize, and collaborate on files from anywhere. - [Carbone](https://composio.dev/toolkits/carbone) - Carbone is a blazing-fast report generator that turns JSON data into PDFs, Word docs, spreadsheets, and more using flexible templates. It lets you automate document creation at scale with minimal code. - [Castingwords](https://composio.dev/toolkits/castingwords) - CastingWords is a transcription service specializing in human-powered, accurate transcripts via a simple API. Get seamless audio-to-text conversion for interviews, meetings, podcasts, and more. - [Cloudconvert](https://composio.dev/toolkits/cloudconvert) - CloudConvert is a powerful file conversion service supporting over 200 file formats. It streamlines converting, compressing, and managing documents, media, and more, all in one place. - [Cloudpress](https://composio.dev/toolkits/cloudpress) - Cloudpress is a content export tool for Google Docs and Notion. It automates publishing to your favorite Content Management Systems. - [Contentful graphql](https://composio.dev/toolkits/contentful_graphql) - Contentful graphql is a content delivery API that lets you access Contentful data using GraphQL queries. It gives you efficient, flexible ways to fetch and manage structured content for any digital project. - [Conversion tools](https://composio.dev/toolkits/conversion_tools) - Conversion Tools is an online service for converting documents between formats such as PDF, Word, Excel, XML, and CSV. It lets you automate complex document workflows with just a few clicks. - [Convertapi](https://composio.dev/toolkits/convertapi) - ConvertAPI is a robust file conversion service for documents, images, and spreadsheets. It streamlines programmatic format changes and lets developers automate complex workflows with a single API. - [Craftmypdf](https://composio.dev/toolkits/craftmypdf) - CraftMyPDF is a web-based service for designing and generating PDFs with templates and live data. It streamlines document creation by automating personalized PDFs at scale. - [Docmosis](https://composio.dev/toolkits/docmosis) - Docmosis generates PDF and Word documents from user-defined templates. It's perfect for merging data fields to quickly produce reports, invoices, and business letters. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Cloudlayer MCP? With a standalone Cloudlayer MCP server, the agents and LLMs can only access a fixed set of Cloudlayer tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Cloudlayer 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 Cloudlayer tools. ### Can I manage the permissions and scopes for Cloudlayer while using Tool Router? Yes, absolutely. You can configure which Cloudlayer 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 Cloudlayer data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) ยท [Composio docs](https://docs.composio.dev/llms.txt)