# How to integrate Backendless MCP with Pydantic AI ```json { "title": "How to integrate Backendless MCP with Pydantic AI", "toolkit": "Backendless", "toolkit_slug": "backendless", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/backendless/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/backendless/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:02:19.950Z" } ``` ## Introduction This guide walks you through connecting Backendless to Pydantic AI using the Composio tool router. By the end, you'll have a working Backendless agent that can list all files in the user uploads folder, create a new directory for project assets, retrieve users where status is active through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Backendless account through Composio's Backendless MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Backendless with - [OpenAI Agents SDK](https://composio.dev/toolkits/backendless/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/backendless/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/backendless/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/backendless/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/backendless/framework/codex) - [OpenClaw](https://composio.dev/toolkits/backendless/framework/openclaw) - [Hermes](https://composio.dev/toolkits/backendless/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/backendless/framework/cli) - [Google ADK](https://composio.dev/toolkits/backendless/framework/google-adk) - [LangChain](https://composio.dev/toolkits/backendless/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/backendless/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/backendless/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/backendless/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/backendless/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 Backendless - 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 Backendless 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 Backendless MCP server, and what's possible with it? The Backendless MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Backendless account. It provides structured and secure access to your backend services, so your agent can perform actions like managing file storage, retrieving and updating database records, handling directories, and orchestrating server-side logic on your behalf. - Dynamic file and directory management: Allow your agent to create, copy, delete, and list files or folders in your Backendless storage, keeping your app data organized. - Database record retrieval and filtering: Empower the agent to fetch objects from specific tables with advanced filtering, sorting, and pagination for instant data access. - Automated backend task scheduling: Let the agent create or delete timers to run recurring or one-off server-side logic, enabling powerful backend automation. - Custom Hive resource management: Instruct your agent to create new Backendless Hive resources and retrieve full maps of stored values for scalable, flexible data handling. - Safe data cleanup: Make it easy for your agent to remove obsolete files, directories, or scheduled tasks, helping maintain a tidy and efficient backend environment. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `BACKENDLESS_COPY_FILE` | Copy File | Tool to copy a file or directory within Backendless file storage. Use when duplicating files to a new location after verifying source and destination paths. | | `BACKENDLESS_CREATE_DIRECTORY` | Create Directory | Tool to create a new directory at the specified path. Use when you need to organize files under a new folder structure. | | `BACKENDLESS_CREATE_HIVE` | Create Backendless Hive | Tool to create a new Hive. Use when you need to provision a new Hive resource before performing Hive operations. Example: Create a hive named 'groceryStore'. | | `BACKENDLESS_CREATE_TIMER` | Create Backendless Timer | Tool to create a new timer with schedule and code. Use when scheduling recurring or one-off tasks to run server-side logic after confirming parameters. | | `BACKENDLESS_DELETE_DIRECTORY` | Delete Directory | Tool to delete a directory at the specified path in Backendless file storage. Use when you need to remove folders after confirming the path. | | `BACKENDLESS_DELETE_FILE` | Delete File | Deletes a file from Backendless file storage at the specified path. Use this tool when you need to remove files from storage. The operation is permanent and cannot be undone. Ensure the file path is correct before deletion. | | `BACKENDLESS_DELETE_TIMER` | Delete Backendless Timer | Deletes a Backendless timer by its unique name. Use this tool to permanently remove a scheduled timer from your Backendless application. The timer must exist and you must provide its exact name. Once deleted, the timer's scheduled executions will stop immediately and cannot be recovered. Note: Requires access to Backendless Console Management API (available with Plus or Enterprise plans). | | `BACKENDLESS_DIRECTORY_LISTING` | Directory Listing | Tool to retrieve a listing of files and directories at a given path. Use when browsing or filtering file storage directories. | | `BACKENDLESS_GENERAL_OBJECT_RETRIEVAL` | General Object Retrieval | Tool to retrieve objects from a specified Backendless table with filtering, sorting, and pagination. Use after confirming the table name and query options. Example: "Get Users where age > 30 sorted by created desc". | | `BACKENDLESS_GET_ALL_VALUES` | Get All Values | Tool to retrieve all values from a map in a specified Hive. Use when you need to fetch the entire contents of a Hive map at once. | | `BACKENDLESS_GET_COUNTER_VALUE` | Get Counter Value | Tool to retrieve the current value of a Backendless counter. Use when you need to inspect an atomic counter's value. | | `BACKENDLESS_GET_FILE_COUNT` | Get File Count | Tool to get the count of files in a Backendless directory. Use when you need to determine how many items match a filter or include subdirectories. | | `BACKENDLESS_GET_KEY_ITEMS` | Get Key Items | Tool to retrieve values for a specified key in a list (all, single, or range). Use when you need specific elements or the entire list from a Hive key. Supports single index retrieval, range retrieval, or full list. | | `BACKENDLESS_GET_TIMER` | Get Backendless Timer | Tool to retrieve information about a specific timer. Use when you need to inspect a timer's schedule and next run details by name. | | `BACKENDLESS_MAP_PUT` | Map Put | Tool to set or update key-value pairs in a Hive map. Use when you need to add or update multiple entries in a Hive map. | | `BACKENDLESS_MOVE_FILE` | Move File | Tool to move a file or directory within Backendless file storage. Use when relocating resources to a new path after verifying source and destination. | | `BACKENDLESS_PUBLISH_MESSAGE` | Publish Message | Tool to publish a message to a specified messaging channel. Use when you need to send notifications or events to subscribers after confirming channel and payload. | | `BACKENDLESS_RESET_COUNTER` | Reset Counter | Tool to reset a Backendless counter back to zero. Use when you need to reinitialize a counter before starting a new sequence. | | `BACKENDLESS_SET_COUNTER_VALUE` | Set Counter Value | Tool to set a Backendless counter to a specific value conditionally. Use when you need to ensure the counter only updates if it currently matches an expected value. | | `BACKENDLESS_UPDATE_TIMER` | Update Backendless Timer | Tool to update schedule or code of an existing timer. Use when you need to modify a timer's configuration after retrieval. | | `BACKENDLESS_USER_DELETE` | Delete User | Tool to delete a user by user ID. Use when removing a user account after confirming permissions. | | `BACKENDLESS_USER_FIND` | Find User by ID | Tool to retrieve user information by ID. Use when you need to fetch details for a specific user after you have their objectId. | | `BACKENDLESS_USER_GRANT_PERMISSION` | Grant Permission to User | Tool to grant a permission to a user on a specific data object. Use when precise access rights must be assigned after verifying the table and object IDs. Example: "Grant FIND permission to a user for a Person record". | | `BACKENDLESS_USER_LOGIN` | User Login | Tool to log in a registered user with identity and password. Use when you need to authenticate a user before making subsequent requests. Example: "Login alice@wonderland.com with password wonderland". | | `BACKENDLESS_USER_LOGOUT` | User Logout | Tool to log out the currently authenticated user. Use when you need to terminate the user session after operations. | | `BACKENDLESS_USER_PASSWORD_RECOVERY` | User Password Recovery | Tool to initiate password recovery for a user. Use when a user requests a password reset after forgetting their password. Triggers an email with recovery instructions. | | `BACKENDLESS_USER_REGISTRATION` | User Registration | Tool to register a new user with email and password. Use when creating a user account or converting a guest account to a registered one after collecting credentials. Example: Register 'alice@wonderland.com' with password 'wonderland'. | | `BACKENDLESS_USER_REVOKE_PERMISSION` | Revoke Permission from User | Tool to revoke a permission from a specified user or role on a specific data object. Use when you need to deny a previously granted operation for a user or role on a data object after verifying the table and object IDs. | | `BACKENDLESS_USER_UPDATE` | Update User | Tool to update properties of an existing Backendless user. Use when you need to modify user profile fields after login. Example: Update phoneNumber to "5551212". | | `BACKENDLESS_VALIDATE_USER_TOKEN` | Validate User Token | Tool to validate a user session token. Use after obtaining a token from login to confirm the session is active. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Backendless MCP server is an implementation of the Model Context Protocol that connects your AI agent to Backendless. It provides structured and secure access so your agent can perform Backendless 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 Backendless - 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 Backendless - MCPServerStreamableHTTP connects to the Backendless 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 Backendless 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 Backendless composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["backendless"], ) 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 Backendless endpoint - The agent uses GPT-5 to interpret user commands and perform Backendless operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent backendless_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[backendless_mcp], instructions=( "You are a Backendless assistant. Use Backendless 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 - Backendless 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 Backendless.\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 Backendless composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["backendless"], ) 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 backendless_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[backendless_mcp], instructions=( "You are a Backendless assistant. Use Backendless 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 Backendless.\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 Backendless through Composio's Tool Router. With this setup, your agent can perform real Backendless 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 + Backendless 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 Backendless MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/backendless/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/backendless/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/backendless/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/backendless/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/backendless/framework/codex) - [OpenClaw](https://composio.dev/toolkits/backendless/framework/openclaw) - [Hermes](https://composio.dev/toolkits/backendless/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/backendless/framework/cli) - [Google ADK](https://composio.dev/toolkits/backendless/framework/google-adk) - [LangChain](https://composio.dev/toolkits/backendless/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/backendless/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/backendless/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/backendless/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/backendless/framework/crew-ai) ## Related Toolkits - [Supabase](https://composio.dev/toolkits/supabase) - Supabase is an open-source backend platform offering scalable Postgres databases, authentication, storage, and real-time APIs. It lets developers build modern apps without managing infrastructure. - [Codeinterpreter](https://composio.dev/toolkits/codeinterpreter) - Codeinterpreter is a Python-based coding environment with built-in data analysis and visualization. It lets you instantly run scripts, plot results, and prototype solutions inside supported platforms. - [GitHub](https://composio.dev/toolkits/github) - GitHub is a code hosting platform for version control and collaborative software development. It streamlines project management, code review, and team workflows in one place. - [Ably](https://composio.dev/toolkits/ably) - Ably is a real-time messaging platform for live chat and data sync in modern apps. It offers global scale and rock-solid reliability for seamless, instant experiences. - [Abuselpdb](https://composio.dev/toolkits/abuselpdb) - Abuselpdb is a central database for reporting and checking IPs linked to malicious online activity. Use it to quickly identify and report suspicious or abusive IP addresses. - [Alchemy](https://composio.dev/toolkits/alchemy) - Alchemy is a blockchain development platform offering APIs and tools for Ethereum apps. It simplifies building and scaling Web3 projects with robust infrastructure. - [Algolia](https://composio.dev/toolkits/algolia) - Algolia is a hosted search API that powers lightning-fast, relevant search experiences for web and mobile apps. It helps developers deliver instant, typo-tolerant, and scalable search without complex infrastructure. - [Anchor browser](https://composio.dev/toolkits/anchor_browser) - Anchor browser is a developer platform for AI-powered web automation. It transforms complex browser actions into easy API endpoints for streamlined web interaction. - [Apiflash](https://composio.dev/toolkits/apiflash) - Apiflash is a website screenshot API for programmatically capturing web pages. It delivers high-quality screenshots on demand for automation, monitoring, or reporting. - [Apiverve](https://composio.dev/toolkits/apiverve) - Apiverve delivers a suite of powerful APIs that simplify integration for developers. It's designed for reliability and scalability so you can build faster, smarter applications without the integration headache. - [Appcircle](https://composio.dev/toolkits/appcircle) - Appcircle is an enterprise-grade mobile CI/CD platform for building, testing, and publishing mobile apps. It streamlines mobile DevOps so teams ship faster and with more confidence. - [Appdrag](https://composio.dev/toolkits/appdrag) - Appdrag is a cloud platform for building websites, APIs, and databases with drag-and-drop tools and code editing. It accelerates development and iteration by combining hosting, database management, and low-code features in one place. - [Appveyor](https://composio.dev/toolkits/appveyor) - AppVeyor is a cloud-based continuous integration service for building, testing, and deploying applications. It helps developers automate and streamline their software delivery pipelines. - [Baserow](https://composio.dev/toolkits/baserow) - Baserow is an open-source no-code database platform for building collaborative data apps. It makes it easy for teams to organize data and automate workflows without writing code. - [Bench](https://composio.dev/toolkits/bench) - Bench is a benchmarking tool for automated performance measurement and analysis. It helps you quickly evaluate, compare, and track your systems or workflows. - [Better stack](https://composio.dev/toolkits/better_stack) - Better Stack is a monitoring, logging, and incident management solution for apps and services. It helps teams ensure application reliability and performance with real-time insights. - [Bitbucket](https://composio.dev/toolkits/bitbucket) - Bitbucket is a Git-based code hosting and collaboration platform for teams. It enables secure repository management and streamlined code reviews. - [Blazemeter](https://composio.dev/toolkits/blazemeter) - Blazemeter is a continuous testing platform for web and mobile app performance. It empowers teams to automate and analyze large-scale tests with ease. - [Blocknative](https://composio.dev/toolkits/blocknative) - Blocknative delivers real-time mempool monitoring and transaction management for public blockchains. Instantly track pending transactions and optimize blockchain interactions with live data. - [Bolt iot](https://composio.dev/toolkits/bolt_iot) - Bolt IoT is a platform for building and managing IoT projects with cloud-based device control and monitoring. It makes connecting sensors and actuators to the internet seamless for automation and data insights. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Backendless MCP? With a standalone Backendless MCP server, the agents and LLMs can only access a fixed set of Backendless tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Backendless 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 Backendless tools. ### Can I manage the permissions and scopes for Backendless while using Tool Router? Yes, absolutely. You can configure which Backendless 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 Backendless 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)