# How to integrate Docmosis MCP with Pydantic AI ```json { "title": "How to integrate Docmosis MCP with Pydantic AI", "toolkit": "Docmosis", "toolkit_slug": "docmosis", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/docmosis/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/docmosis/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:09:06.847Z" } ``` ## Introduction This guide walks you through connecting Docmosis to Pydantic AI using the Composio tool router. By the end, you'll have a working Docmosis agent that can generate monthly invoice pdf for a customer, create personalized offer letters for new hires, produce event registration forms as word docs through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Docmosis account through Composio's Docmosis MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Docmosis with - [OpenAI Agents SDK](https://composio.dev/toolkits/docmosis/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/docmosis/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/docmosis/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/docmosis/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/docmosis/framework/codex) - [OpenClaw](https://composio.dev/toolkits/docmosis/framework/openclaw) - [Hermes](https://composio.dev/toolkits/docmosis/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/docmosis/framework/cli) - [Google ADK](https://composio.dev/toolkits/docmosis/framework/google-adk) - [LangChain](https://composio.dev/toolkits/docmosis/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/docmosis/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/docmosis/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/docmosis/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/docmosis/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 Docmosis - 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 Docmosis 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 Docmosis MCP server, and what's possible with it? The Docmosis MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Docmosis account. It provides structured and secure access to your document templates and generation capabilities, so your agent can perform actions like generating documents, merging data fields, exporting PDFs or Word files, and automating report creation on your behalf. - Dynamic document generation: Instantly create PDF or Word documents from pre-built templates by merging in your custom data fields. - Automated report and invoice creation: Let your agent assemble business reports, invoices, or letters using real-time input and reusable templates. - Template management and selection: Retrieve, list, and select from available templates for different document types or business needs. - Batch document processing: Generate multiple documents at once by feeding bulk data sets—perfect for automating repetitive paperwork. - Flexible file export and delivery: Export generated documents in your preferred format and deliver them to specified locations, systems, or users automatically. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `DOCMOSIS_DELETE_IMAGE` | Docmosis: Delete Image(s) | Tool to delete one or more stored images. Use when you need to remove images; ensure imageName(s) are valid before use. | | `DOCMOSIS_DELETE_TEMPLATE` | Docmosis: Delete Template(s) | Tool to delete one or more templates from the environment. Use when you need to remove templates; multiple templates can be deleted in a single request. | | `DOCMOSIS_ENVIRONMENT_READY` | Docmosis Environment Ready | Tool to verify environment readiness. Use when ensuring the environment is active and within quota before rendering documents. | | `DOCMOSIS_ENVIRONMENT_SUMMARY` | Docmosis Environment Summary | Tool to retrieve environment summary. Use when you need status, plan, and quota details of your Docmosis environment after authentication. | | `DOCMOSIS_GET_API_KEY` | Docmosis: Get API Key | Tool to extract the Docmosis API access key from connection metadata. Use before other Docmosis API calls to retrieve the Bearer token from the Authorization header. | | `DOCMOSIS_GET_BATCH_UPLOAD_STATUS` | Get Batch Upload Status | Tool to check the status of a template batch upload job. Use when monitoring batch upload progress or checking if a batch upload has completed. | | `DOCMOSIS_GET_IMAGE` | Download Docmosis Images | Tool to download one or more images. Use when you need to retrieve stored image files by name. If multiple names provided, images are returned in a zip archive. | | `DOCMOSIS_GET_RENDER_QUEUE` | Get Docmosis Render Queue | Tool to get current render queue status and utilization. Use when monitoring queue capacity before scheduling rendering tasks. | | `DOCMOSIS_GET_RENDER_TAGS` | Get Render Tags | Tool to retrieve statistics on renders tagged with user-defined phrases. Returns page counts and document counts aggregated monthly. Use when reporting activity of user groups or features. | | `DOCMOSIS_GET_SAMPLE_DATA` | Get Template Sample Data | Tool to generate sample data for a Docmosis template based on its structure. Creates placeholder values that can be used for testing renders. Returns data in JSON or XML format. | | `DOCMOSIS_GET_TEMPLATE` | Download Docmosis Templates | Tool to retrieve originally uploaded templates. Use when you need to download template files by name. If multiple names provided (up to 100), templates are returned in a zip archive. | | `DOCMOSIS_GET_TEMPLATE_DETAILS` | Get Docmosis Template Details | Tool to retrieve metadata for an uploaded template. Returns name, size, MD5 hash, last modified date, and error status. Use after uploading a template to verify it was stored correctly or to check if it has errors. | | `DOCMOSIS_GET_TEMPLATE_STRUCTURE` | Get Docmosis Template Structure | Tool to retrieve a template's parsed structure: fields, repeats, conditions, images, and refs. Use after uploading a template to inspect its JSON structure. | | `DOCMOSIS_LIST_IMAGES` | Docmosis: List Images | Tool to list available stock images. Use when you need to retrieve image names optionally filtered by folder. | | `DOCMOSIS_LIST_TEMPLATES` | Docmosis: List Templates | Tool to list all templates available in the environment. Use when you need to retrieve template names, optionally filtered by folder with pagination support. | | `DOCMOSIS_PING` | Docmosis Ping | Tool to check connectivity to Docmosis Cloud services. Use when validating that the service endpoint is reachable before other operations. | | `DOCMOSIS_PING_DOCMOSIS_SERVICE` | Ping Docmosis Service | Tool to check that Docmosis Cloud services are online and at least one server is listening. Use for diagnostics and monitoring to verify service availability. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Docmosis MCP server is an implementation of the Model Context Protocol that connects your AI agent to Docmosis. It provides structured and secure access so your agent can perform Docmosis 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 Docmosis - 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 Docmosis - MCPServerStreamableHTTP connects to the Docmosis 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 Docmosis 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 Docmosis composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["docmosis"], ) 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 Docmosis endpoint - The agent uses GPT-5 to interpret user commands and perform Docmosis operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent docmosis_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[docmosis_mcp], instructions=( "You are a Docmosis assistant. Use Docmosis 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 - Docmosis 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 Docmosis.\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 Docmosis composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["docmosis"], ) 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 docmosis_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[docmosis_mcp], instructions=( "You are a Docmosis assistant. Use Docmosis 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 Docmosis.\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 Docmosis through Composio's Tool Router. With this setup, your agent can perform real Docmosis 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 + Docmosis 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 Docmosis MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/docmosis/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/docmosis/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/docmosis/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/docmosis/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/docmosis/framework/codex) - [OpenClaw](https://composio.dev/toolkits/docmosis/framework/openclaw) - [Hermes](https://composio.dev/toolkits/docmosis/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/docmosis/framework/cli) - [Google ADK](https://composio.dev/toolkits/docmosis/framework/google-adk) - [LangChain](https://composio.dev/toolkits/docmosis/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/docmosis/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/docmosis/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/docmosis/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/docmosis/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. - [Cloudlayer](https://composio.dev/toolkits/cloudlayer) - Cloudlayer is a document and asset generation service for creating PDFs and images via API or SDKs. It lets you automate high-quality doc creation, saving dev time and reducing manual work. - [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. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Docmosis MCP? With a standalone Docmosis MCP server, the agents and LLMs can only access a fixed set of Docmosis tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Docmosis 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 Docmosis tools. ### Can I manage the permissions and scopes for Docmosis while using Tool Router? Yes, absolutely. You can configure which Docmosis 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 Docmosis data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)