# How to integrate Piggy MCP with Pydantic AI ```json { "title": "How to integrate Piggy MCP with Pydantic AI", "toolkit": "Piggy", "toolkit_slug": "piggy", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/piggy/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/piggy/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:21:56.571Z" } ``` ## Introduction This guide walks you through connecting Piggy to Pydantic AI using the Composio tool router. By the end, you'll have a working Piggy agent that can check your current piggy loyalty points, redeem points for a store discount, list recent cashback transactions through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Piggy account through Composio's Piggy MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Piggy with - [OpenAI Agents SDK](https://composio.dev/toolkits/piggy/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/piggy/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/piggy/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/piggy/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/piggy/framework/codex) - [OpenClaw](https://composio.dev/toolkits/piggy/framework/openclaw) - [Hermes](https://composio.dev/toolkits/piggy/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/piggy/framework/cli) - [Google ADK](https://composio.dev/toolkits/piggy/framework/google-adk) - [LangChain](https://composio.dev/toolkits/piggy/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/piggy/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/piggy/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/piggy/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/piggy/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 Piggy - 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 Piggy 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 Piggy MCP server, and what's possible with it? The Piggy MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Piggy account. It provides structured and secure access to your loyalty and rewards programs, so your agent can perform actions like managing customer points, handling cashback offers, updating loyalty rewards, and analyzing user engagement on your behalf. - Customer points management: Let your agent track, update, and redeem customer loyalty points across your online store. - Automated cashback processing: Enable your agent to issue, adjust, or report cashback rewards to users as part of promotional campaigns. - Loyalty reward configuration: Allow your agent to set up, modify, or deactivate loyalty program rewards to keep your offers fresh and competitive. - User engagement analytics: Have your agent analyze program participation to help identify top customers and optimize retention strategies. - Discount and offer management: Let your agent create or update custom discounts and promotional offers linked to your loyalty program. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `PIGGY_CLAIM_ANONYMOUS_CONTACT` | Claim Anonymous Contact | Tool to claim an anonymous contact by associating it with a real email address. Use when converting an anonymous contact (with a fictional email) into a verified contact with a real email address. | | `PIGGY_CREATE_CONTACT_ATTRIBUTE` | Create Contact Attribute | Tool to create a custom Contact Attribute. Use when you need to define new fields for contacts after initial setup. | | `PIGGY_CREATE_CREDIT_RECEPTION` | Create Credit Reception | Tool to create a credit reception for a contact. Use when awarding credits to customers based on purchases or fixed amounts. | | `PIGGY_CREATE_VOUCHERS_BATCH` | Create Vouchers Batch | Tool to create a batch of vouchers for a promotion. Use when you need to generate multiple vouchers at once for a specific promotion. Batch processing is asynchronous and returns a PENDING status initially. | | `PIGGY_FIND_OR_CREATE_PRODUCTS` | Find or Create Products | Tool to find an existing product by external_identifier or create a new one if it doesn't exist. Use when you need to ensure a product exists in Piggy's system for loyalty programs or rewards. | | `PIGGY_FIND_VOUCHER_BY_CODE` | Find Voucher By Code | Tool to find a voucher by its unique code. Use when you need to retrieve voucher details, check redemption status, or validate a voucher code. | | `PIGGY_GET_CONTACT_AUTH_TOKEN` | Get Contact Auth Token | Tool to get an auth token for a Contact. Use after obtaining a Contact UUID and needing to verify identity for secure operations. | | `PIGGY_GET_CONTACTS_CREDIT_BALANCE` | Get Contact's Credit Balance | Tool to get a Contact's credit balance. Use when you need to check a contact's current credit balance before processing rewards or promotions. | | `PIGGY_LIST_FORMS` | List Forms | Tool to list all forms in the Piggy account. Use when you need to retrieve available forms for customer interactions. | | `PIGGY_LIST_PERKS` | List Perks | Tool to list all available perks in Piggy. Use when you need to retrieve the catalog of perks that can be associated with contacts or transactions. | | `PIGGY_MERGE_CONTACTS` | Merge Contacts | Merges a source contact into a destination contact in Piggy's CRM. The source contact's data (attributes, balances, transactions) is transferred to the destination contact, and the source contact is removed. This operation is irreversible and processed asynchronously via a job queue. Use this when consolidating duplicate customer records. | | `PIGGY_SEND_CONTACT_VERIFICATION_EMAIL` | Send Contact Verification Email | Send a verification email to a Piggy contact for identity verification. The contact must exist in the system with a configured Contacts Portal. Returns success message with email sent confirmation. Use this when implementing email-based authentication workflows or when contacts need to verify their email address to access the Contacts Portal. | | `PIGGY_UPDATE_BOOKINGS` | Update Bookings | Tool to update an existing booking in Piggy. Use when you need to modify booking details such as party size, status, or company name. Note: Shop and contact cannot be updated after creation. | | `PIGGY_UPDATE_CONTACT_IDENTIFIERS` | Update Contact Identifiers | Tool to update a contact identifier in Piggy. Use when you need to modify the display name or active state of an existing contact identifier. Only the name and active properties can be updated; the identifier value itself cannot be changed. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Piggy MCP server is an implementation of the Model Context Protocol that connects your AI agent to Piggy. It provides structured and secure access so your agent can perform Piggy 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 Piggy - 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 Piggy - MCPServerStreamableHTTP connects to the Piggy 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 Piggy 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 Piggy composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["piggy"], ) 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 Piggy endpoint - The agent uses GPT-5 to interpret user commands and perform Piggy operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent piggy_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[piggy_mcp], instructions=( "You are a Piggy assistant. Use Piggy 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 - Piggy 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 Piggy.\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 Piggy composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["piggy"], ) 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 piggy_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[piggy_mcp], instructions=( "You are a Piggy assistant. Use Piggy 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 Piggy.\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 Piggy through Composio's Tool Router. With this setup, your agent can perform real Piggy 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 + Piggy 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 Piggy MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/piggy/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/piggy/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/piggy/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/piggy/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/piggy/framework/codex) - [OpenClaw](https://composio.dev/toolkits/piggy/framework/openclaw) - [Hermes](https://composio.dev/toolkits/piggy/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/piggy/framework/cli) - [Google ADK](https://composio.dev/toolkits/piggy/framework/google-adk) - [LangChain](https://composio.dev/toolkits/piggy/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/piggy/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/piggy/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/piggy/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/piggy/framework/crew-ai) ## Related Toolkits - [Reddit](https://composio.dev/toolkits/reddit) - Reddit is a social news platform with thriving user-driven communities (subreddits). It's the go-to place for discussion, content sharing, and viral marketing. - [Facebook](https://composio.dev/toolkits/facebook) - Facebook is a social media and advertising platform for businesses and creators. It helps you connect, share, and manage content across your public Facebook Pages. - [Linkedin](https://composio.dev/toolkits/linkedin) - LinkedIn is a professional networking platform for connecting, sharing content, and engaging with business opportunities. It's the go-to place for building your professional brand and unlocking new career connections. - [Active campaign](https://composio.dev/toolkits/active_campaign) - ActiveCampaign is a marketing automation and CRM platform for managing email campaigns, sales pipelines, and customer segmentation. It helps businesses engage customers and drive growth through smart automation and targeted outreach. - [ActiveTrail](https://composio.dev/toolkits/active_trail) - ActiveTrail is a user-friendly email marketing and automation platform. It helps you reach subscribers and automate campaigns with ease. - [Ahrefs](https://composio.dev/toolkits/ahrefs) - Ahrefs is an SEO and marketing platform for site audits, keyword research, and competitor insights. It helps you improve search rankings and drive organic traffic. - [Amcards](https://composio.dev/toolkits/amcards) - AMCards lets you create and mail personalized greeting cards online. Build stronger customer relationships with easy, automated card campaigns. - [Beamer](https://composio.dev/toolkits/beamer) - Beamer is a news and changelog platform for in-app announcements and feature updates. It helps companies boost user engagement by sharing news where users are most active. - [Benchmark email](https://composio.dev/toolkits/benchmark_email) - Benchmark Email is a platform for creating, sending, and tracking email campaigns. It's built to help you engage audiences and analyze results—all in one place. - [Bigmailer](https://composio.dev/toolkits/bigmailer) - BigMailer is an email marketing platform for managing multiple brands with white-labeling and automation. It helps teams streamline campaigns and simplify integration with Amazon SES. - [Brandfetch](https://composio.dev/toolkits/brandfetch) - Brandfetch is an API that delivers company logos, colors, and visual branding assets. It helps marketers and developers keep brand visuals consistent everywhere. - [Brevo](https://composio.dev/toolkits/brevo) - Brevo is an all-in-one email and SMS marketing platform for transactional messaging, automation, and CRM. It helps businesses engage customers and streamline communications through powerful campaign tools. - [Campayn](https://composio.dev/toolkits/campayn) - Campayn is an email marketing platform for creating, sending, and managing campaigns. It helps businesses engage contacts and grow audiences with easy-to-use tools. - [Cardly](https://composio.dev/toolkits/cardly) - Cardly is a platform for creating and sending personalized direct mail to customers. It helps businesses break through the digital clutter by getting real engagement via physical mailboxes. - [ClickSend](https://composio.dev/toolkits/clicksend) - ClickSend is a cloud-based SMS and email marketing platform for businesses. It streamlines communication by enabling quick message delivery and contact management. - [Crustdata](https://composio.dev/toolkits/crustdata) - CrustData is an AI-powered data intelligence platform for real-time company and people data. It helps B2B sales teams, AI SDRs, and investors react to live business signals. - [Curated](https://composio.dev/toolkits/curated) - Curated is a platform for collecting, curating, and publishing newsletters. It streamlines content aggregation and distribution for creators and teams. - [Customerio](https://composio.dev/toolkits/customerio) - Customer.io is a customer engagement platform for targeted messaging across email, SMS, and push. Easily automate, segment, and track communications with your audience. - [Cutt ly](https://composio.dev/toolkits/cutt_ly) - Cutt.ly is a URL shortening service for managing and analyzing links. Streamline your workflows with quick, trackable, and branded short URLs. - [Demio](https://composio.dev/toolkits/demio) - Demio is webinar software built for marketers, offering both live and automated sessions with interactive features. It helps teams engage audiences and optimize lead generation through detailed analytics. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Piggy MCP? With a standalone Piggy MCP server, the agents and LLMs can only access a fixed set of Piggy tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Piggy 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 Piggy tools. ### Can I manage the permissions and scopes for Piggy while using Tool Router? Yes, absolutely. You can configure which Piggy 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 Piggy data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)