# How to integrate Crowdin MCP with Pydantic AI ```json { "title": "How to integrate Crowdin MCP with Pydantic AI", "toolkit": "Crowdin", "toolkit_slug": "crowdin", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/crowdin/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/crowdin/framework/pydantic-ai.md", "updated_at": "2026-05-06T08:07:38.198Z" } ``` ## Introduction This guide walks you through connecting Crowdin to Pydantic AI using the Composio tool router. By the end, you'll have a working Crowdin agent that can create a new crowdin project for our app, add new source file to the translations project, assign sprint label to specific string ids through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Crowdin account through Composio's Crowdin MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Crowdin with - [OpenAI Agents SDK](https://composio.dev/toolkits/crowdin/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/crowdin/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/crowdin/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/crowdin/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/crowdin/framework/codex) - [OpenClaw](https://composio.dev/toolkits/crowdin/framework/openclaw) - [Hermes](https://composio.dev/toolkits/crowdin/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/crowdin/framework/cli) - [Google ADK](https://composio.dev/toolkits/crowdin/framework/google-adk) - [LangChain](https://composio.dev/toolkits/crowdin/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/crowdin/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/crowdin/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/crowdin/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/crowdin/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 Crowdin - 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 Crowdin 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 Crowdin MCP server, and what's possible with it? The Crowdin MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Crowdin account. It provides structured and secure access to your localization projects, so your agent can manage branches, organize files, label content, automate webhooks, and orchestrate translation workflows on your behalf. - Branch and project management: Easily have your agent create, delete, or organize Crowdin projects and branches to streamline new releases or features. - Dynamic file handling: Let your agent add new files to projects, ensuring your translation assets are always up to date and properly organized by branch or directory. - Labeling and content categorization: Direct your agent to create, assign, or remove labels on resources and strings, helping you segment and track translation tasks with precision. - Workflow automation with webhooks: Automate your translation process by having the agent set up or remove webhooks for real-time notifications and integrations. - Resource cleanup and maintenance: Empower your agent to delete obsolete branches, labels, webhooks, or entire projects, keeping your Crowdin workspace clean and focused. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `CROWDIN_ADD_BRANCH` | Add Branch | Tool to create a new branch in a crowdin project. use when you need to isolate translations for a new feature or release. | | `CROWDIN_ADD_FILE` | Add File | Tool to add a new file to a crowdin project. use after uploading the file to storage to place it under the specified project, branch, or directory. | | `CROWDIN_ADD_LABEL` | Add Label | Tool to create a new label in a crowdin project. use when you need to tag resources with a custom identifier, such as 'sprint-5'. | | `CROWDIN_ADD_PROJECT` | Create Crowdin Project | Tool to create a new project in crowdin. use before uploading source files to initialize translation workflows. | | `CROWDIN_ADD_WEBHOOK` | Add Webhook | Tool to create a new webhook in a crowdin project. use after confirming the project id and desired event triggers. | | `CROWDIN_ASSIGN_LABEL_TO_STRINGS` | Assign Label to Strings | Tool to assign the specified label to provided string ids in a project. use after creating the label or verifying string ids to categorize content. | | `CROWDIN_DELETE_BRANCH` | Delete Branch | Tool to delete a specific branch from a crowdin project. use when you need to remove an obsolete branch after it's fully merged. | | `CROWDIN_DELETE_LABEL` | Delete Label | Tool to delete the label identified by the specified label id in a project. use when you need to remove outdated or incorrect labels. ensure no resources reference the label before deletion. | | `CROWDIN_DELETE_PROJECT` | Delete Project | Tool to delete a crowdin project by its id. use when you need to permanently remove a project after confirming no further usage. ensure all resources are no longer needed before deletion. | | `CROWDIN_DELETE_WEBHOOK` | Delete Webhook | Tool to delete the webhook identified by the specified webhook id in a crowdin project. use when you need to remove obsolete or incorrect webhooks after confirming project and webhook ids. | | `CROWDIN_EDIT_FILE` | Edit File | Tool to update file details in a project. use after confirming valid project and file ids. | | `CROWDIN_EDIT_LABEL` | Edit Label | Tool to edit a label in a crowdin project. use when you need to update the name or description of an existing label. ensure the label exists before using. example: edit label 42 to 'release-1.1'. | | `CROWDIN_EDIT_PROJECT` | Edit Project | Tool to update project details using json-patch. use after confirming project settings to modify metadata like name, description, visibility, or languages. | | `CROWDIN_EDIT_STRING` | Edit String | Tool to update string details in a crowdin project. use when you need to modify a string's text or metadata after creation. | | `CROWDIN_GET_LABEL` | Get Label | Tool to retrieve information about the label identified by the specified label id in a project. use after confirming the project context to fetch label details. | | `CROWDIN_GET_LANGUAGE` | Get Language | Tool to retrieve details of a specific language. use when you have a language identifier and need locale codes and plural rules before configuring translations. | | `CROWDIN_GET_MEMBER_INFO` | Get Member Info | Tool to retrieve information about a project member. use when you need to inspect details for a specific user within a project after obtaining their member id. | | `CROWDIN_GET_PROJECT` | Get Project | Tool to retrieve details of a specific crowdin project. use when you need to inspect project settings before making updates. | | `CROWDIN_GET_STRING` | Get String | Tool to retrieve details of a specific string in a crowdin project. use after confirming the project and string ids to fetch its metadata. | | `CROWDIN_GET_WEBHOOK` | Get Webhook | Tool to retrieve information about the webhook identified by the specified webhook id in a project. use after confirming the project context to fetch webhook details. | | `CROWDIN_LIST_BRANCHES` | List Branches | Tool to list all branches in a crowdin project. use after selecting a project to view its branch structure. supports pagination and optional filtering by branch id. | | `CROWDIN_LIST_FILES` | List Files | Tool to list files in a crowdin project. use when you need to retrieve a list of project files with optional filters by directory, group, or branch before processing. | | `CROWDIN_LIST_LABELS` | List Labels | Tool to list labels in a crowdin project. use when you need to retrieve all labels for a specific project with optional pagination. | | `CROWDIN_LIST_LANGUAGES` | List Languages | Tool to retrieve a list of supported languages. use when you need to fetch all languages crowdin supports before starting localization. | | `CROWDIN_LIST_PROJECT_MEMBERS` | List Project Members | Tool to list members in a crowdin project. use when you need to retrieve project member list for management tasks after confirming the project id. | | `CROWDIN_LIST_PROJECTS` | List Projects | Tool to retrieve a list of all crowdin projects with optional filters. use when you need to paginate through or filter projects by owner, group, language inclusion, or archive status. | | `CROWDIN_LIST_REPORTS` | List Reports | Tool to list reports for a given crowdin project. use after confirming project id to retrieve available reports. supports pagination via limit and offset. | | `CROWDIN_UPLOAD_STORAGE` | Upload Storage | Tool to upload a file to crowdin storage. use when you need to obtain a storageid for further operations like adding files to a project. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Crowdin MCP server is an implementation of the Model Context Protocol that connects your AI agent to Crowdin. It provides structured and secure access so your agent can perform Crowdin 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 Crowdin - 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 Crowdin - MCPServerStreamableHTTP connects to the Crowdin 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 Crowdin 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 Crowdin composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["crowdin"], ) 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 Crowdin endpoint - The agent uses GPT-5 to interpret user commands and perform Crowdin operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent crowdin_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[crowdin_mcp], instructions=( "You are a Crowdin assistant. Use Crowdin 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 - Crowdin 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 Crowdin.\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 Crowdin composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["crowdin"], ) 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 crowdin_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[crowdin_mcp], instructions=( "You are a Crowdin assistant. Use Crowdin 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 Crowdin.\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 Crowdin through Composio's Tool Router. With this setup, your agent can perform real Crowdin 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 + Crowdin 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 Crowdin MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/crowdin/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/crowdin/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/crowdin/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/crowdin/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/crowdin/framework/codex) - [OpenClaw](https://composio.dev/toolkits/crowdin/framework/openclaw) - [Hermes](https://composio.dev/toolkits/crowdin/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/crowdin/framework/cli) - [Google ADK](https://composio.dev/toolkits/crowdin/framework/google-adk) - [LangChain](https://composio.dev/toolkits/crowdin/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/crowdin/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/crowdin/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/crowdin/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/crowdin/framework/crew-ai) ## Related Toolkits - [Apilio](https://composio.dev/toolkits/apilio) - Apilio is a home automation platform that lets you connect and control smart devices from different brands. It helps you build flexible automations with complex conditions, schedules, and integrations. - [Basin](https://composio.dev/toolkits/basin) - Basin is a no-code form backend for quickly setting up reliable contact forms. It lets you collect and manage form submissions without writing any server-side code. - [Bouncer](https://composio.dev/toolkits/bouncer) - Bouncer is an email validation platform that verifies the authenticity of email addresses in real-time and batch. It helps boost deliverability and reduce bounce rates for your communications. - [Conveyor](https://composio.dev/toolkits/conveyor) - Conveyor is a platform that automates security reviews with a Trust Center and AI-driven questionnaire automation. It streamlines compliance and vendor security processes for faster, hassle-free reviews. - [Databox](https://composio.dev/toolkits/databox) - Databox is a business analytics platform that connects your data from any tool and device. It helps you track KPIs, build dashboards, and discover actionable insights. - [Detrack](https://composio.dev/toolkits/detrack) - Detrack is a delivery management platform for real-time tracking and proof of delivery. It helps businesses automate notifications and keep customers updated every step of the way. - [Dnsfilter](https://composio.dev/toolkits/dnsfilter) - Dnsfilter is a cloud-based DNS security and content filtering solution. It helps organizations block online threats and manage safe internet access with ease. - [Faraday](https://composio.dev/toolkits/faraday) - Faraday lets you embed AI in workflows across your stack for smarter automation. It boosts your favorite tools with actionable intelligence and seamless integration. - [Feathery](https://composio.dev/toolkits/feathery) - Feathery is an AI-powered platform for building dynamic data intake forms with advanced logic. It helps teams automate complex workflows and collect structured data with ease. - [Fillout forms](https://composio.dev/toolkits/fillout_forms) - Fillout forms is an online platform for building and managing forms with a flexible API. It lets you create, distribute, and collect responses from forms with ease. - [Formdesk](https://composio.dev/toolkits/formdesk) - Formdesk is an online form builder for creating and managing professional forms. It's perfect for collecting data, automating workflows, and integrating form submissions with your favorite services. - [Formsite](https://composio.dev/toolkits/formsite) - Formsite lets you build online forms and surveys with drag-and-drop simplicity. Capture, manage, and integrate form responses securely for streamlined workflows. - [Graphhopper](https://composio.dev/toolkits/graphhopper) - GraphHopper is an enterprise-grade Directions API for routing, optimization, and geocoding across multiple vehicle types. It enables fast, reliable route planning and logistics automation for businesses. - [Hyperbrowser](https://composio.dev/toolkits/hyperbrowser) - Hyperbrowser is a next-generation platform for scalable browser automation. It empowers AI agents to interact with web apps, automate workflows, and handle browser sessions at scale. - [La Growth Machine](https://composio.dev/toolkits/lagrowthmachine) - La Growth Machine automates multi-channel sales outreach and routine tasks for sales teams. Streamline your workflow and focus on closing more deals. - [Leverly](https://composio.dev/toolkits/leverly) - Leverly is a workflow automation platform that connects and coordinates actions across your apps. It streamlines repetitive processes so your business runs smoother, faster, and with fewer manual steps. - [Maintainx](https://composio.dev/toolkits/maintainx) - Maintainx is a cloud-based CMMS for centralizing maintenance data, communication, and workflows. It helps organizations streamline maintenance operations and improve team coordination. - [Make](https://composio.dev/toolkits/make) - Make is an automation platform that connects your favorite apps and services. Build powerful, custom workflows without writing code. - [Ntfy](https://composio.dev/toolkits/ntfy) - Ntfy is a notification service to send push messages to phones or desktops. Instantly deliver alerts and updates to users, devices, or teams. - [Persona](https://composio.dev/toolkits/persona) - Persona offers identity infrastructure to automate user verification and compliance. It helps organizations securely verify users and reduce fraud risk. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Crowdin MCP? With a standalone Crowdin MCP server, the agents and LLMs can only access a fixed set of Crowdin tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Crowdin 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 Crowdin tools. ### Can I manage the permissions and scopes for Crowdin while using Tool Router? Yes, absolutely. You can configure which Crowdin 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 Crowdin 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)