# How to integrate Abstract MCP with Pydantic AI ```json { "title": "How to integrate Abstract MCP with Pydantic AI", "toolkit": "Abstract", "toolkit_slug": "abstract", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/abstract/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/abstract/framework/pydantic-ai.md", "updated_at": "2026-05-12T09:59:53.425Z" } ``` ## Introduction This guide walks you through connecting Abstract to Pydantic AI using the Composio tool router. By the end, you'll have a working Abstract agent that can check if this iban is valid, assess risk score for this email, validate if an email address is real through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Abstract account through Composio's Abstract MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Abstract with - [OpenAI Agents SDK](https://composio.dev/toolkits/abstract/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/abstract/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/abstract/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/abstract/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/abstract/framework/codex) - [OpenClaw](https://composio.dev/toolkits/abstract/framework/openclaw) - [Hermes](https://composio.dev/toolkits/abstract/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/abstract/framework/cli) - [Google ADK](https://composio.dev/toolkits/abstract/framework/google-adk) - [LangChain](https://composio.dev/toolkits/abstract/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/abstract/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/abstract/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/abstract/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/abstract/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 Abstract - 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 Abstract 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 Abstract MCP server, and what's possible with it? The Abstract MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Abstract account. It provides structured and secure access to advanced validation and enrichment APIs, so your agent can perform actions like validating IBANs, checking email deliverability, and analyzing email reputation automatically on your behalf. - IBAN validation and verification: Instantly confirm the format and country code of any IBAN number to ensure it's accurate before processing transactions. - Email address deliverability checks: Have your agent verify if an email address is real and deliverable, reducing bounce rates and ensuring reliable communication. - Email reputation assessment: Enrich email addresses with reputation and risk scoring data to assess fraud risk or deliverability before outreach or onboarding. - Automated data enrichment workflows: Let your agent use Abstract's tools to streamline data entry, validation, and enrichment tasks for forms, signups, or CRM systems. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `ABSTRACT_EMAIL_REPUTATION_API` | Email validation and quality check | Validate email addresses and assess their deliverability and quality. Returns comprehensive validation data including format checks, disposable email detection, free provider identification, role-based email detection, MX records verification, and SMTP validation. Use this to verify email addresses before sending, filter out risky or low-quality emails, and improve email deliverability rates. | | `ABSTRACT_GET_VAT_CATEGORIES` | Get VAT Categories | Tool to retrieve VAT rate categories for a specific country, including standard, reduced, and special VAT rates. Use when you need to determine applicable VAT rates for different product/service categories in a country. | | `ABSTRACT_ABSTRACT_IBAN_VALIDATION_API` | Validate IBAN | Tool to validate the format and country code of an IBAN number. Use after collecting an IBAN to ensure it is correctly formatted. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Abstract MCP server is an implementation of the Model Context Protocol that connects your AI agent to Abstract. It provides structured and secure access so your agent can perform Abstract 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 Abstract - 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 Abstract - MCPServerStreamableHTTP connects to the Abstract 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 Abstract 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 Abstract composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["abstract"], ) 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 Abstract endpoint - The agent uses GPT-5 to interpret user commands and perform Abstract operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent abstract_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[abstract_mcp], instructions=( "You are a Abstract assistant. Use Abstract 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 - Abstract 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 Abstract.\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 Abstract composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["abstract"], ) 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 abstract_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[abstract_mcp], instructions=( "You are a Abstract assistant. Use Abstract 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 Abstract.\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 Abstract through Composio's Tool Router. With this setup, your agent can perform real Abstract 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 + Abstract 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 Abstract MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/abstract/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/abstract/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/abstract/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/abstract/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/abstract/framework/codex) - [OpenClaw](https://composio.dev/toolkits/abstract/framework/openclaw) - [Hermes](https://composio.dev/toolkits/abstract/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/abstract/framework/cli) - [Google ADK](https://composio.dev/toolkits/abstract/framework/google-adk) - [LangChain](https://composio.dev/toolkits/abstract/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/abstract/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/abstract/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/abstract/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/abstract/framework/crew-ai) ## Related Toolkits - [Excel](https://composio.dev/toolkits/excel) - Microsoft Excel is a robust spreadsheet application for organizing, analyzing, and visualizing data. It's the go-to tool for calculations, reporting, and flexible data management. - [21risk](https://composio.dev/toolkits/_21risk) - 21RISK is a web app built for easy checklist, audit, and compliance management. It streamlines risk processes so teams can focus on what matters. - [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time. - [Agentql](https://composio.dev/toolkits/agentql) - Agentql is a toolkit that connects AI agents to the web using a specialized query language. It enables structured web interaction and data extraction for smarter automations. - [Agenty](https://composio.dev/toolkits/agenty) - Agenty is a web scraping and automation platform for extracting data and automating browser tasks—no coding needed. It streamlines data collection, monitoring, and repetitive online actions. - [Ambee](https://composio.dev/toolkits/ambee) - Ambee is an environmental data platform providing real-time, hyperlocal APIs for air quality, weather, and pollen. Get precise environmental insights to power smarter decisions in your apps and workflows. - [Ambient weather](https://composio.dev/toolkits/ambient_weather) - Ambient Weather is a platform for personal weather stations with a robust API for accessing local, real-time, and historical weather data. Get detailed environmental insights directly from your own sensors for smarter apps and automations. - [Anonyflow](https://composio.dev/toolkits/anonyflow) - Anonyflow is a service for encryption-based data anonymization and secure data sharing. It helps organizations meet GDPR, CCPA, and HIPAA data privacy compliance requirements. - [Api ninjas](https://composio.dev/toolkits/api_ninjas) - Api ninjas offers 120+ public APIs spanning categories like weather, finance, sports, and more. Developers use it to supercharge apps with real-time data and actionable endpoints. - [Api sports](https://composio.dev/toolkits/api_sports) - Api sports is a comprehensive sports data platform covering 2,000+ competitions with live scores and 15+ years of stats. Instantly access up-to-date sports information for analysis, apps, or chatbots. - [Apify](https://composio.dev/toolkits/apify) - Apify is a cloud platform for building, deploying, and managing web scraping and automation tools called Actors. It lets you automate data extraction and workflow tasks at scale—no infrastructure headaches. - [Autom](https://composio.dev/toolkits/autom) - Autom is a lightning-fast search engine results data platform for Google, Bing, and Brave. Developers use it to access fresh, low-latency SERP data on demand. - [Beaconchain](https://composio.dev/toolkits/beaconchain) - Beaconchain is a real-time analytics platform for Ethereum 2.0's Beacon Chain. It provides detailed insights into validators, blocks, and overall network performance. - [Big data cloud](https://composio.dev/toolkits/big_data_cloud) - BigDataCloud provides APIs for geolocation, reverse geocoding, and address validation. Instantly access reliable location intelligence to enhance your applications and workflows. - [Bigpicture io](https://composio.dev/toolkits/bigpicture_io) - BigPicture.io offers APIs for accessing detailed company and profile data. Instantly enrich your applications with up-to-date insights on 20M+ businesses. - [Bitquery](https://composio.dev/toolkits/bitquery) - Bitquery is a blockchain data platform offering indexed, real-time, and historical data from 40+ blockchains via GraphQL APIs. Get unified, reliable access to complex on-chain data for analytics, trading, and research. - [Brightdata](https://composio.dev/toolkits/brightdata) - Brightdata is a leading web data platform offering advanced scraping, SERP APIs, and anti-bot tools. It lets you collect public web data at scale, bypassing blocks and friction. - [Builtwith](https://composio.dev/toolkits/builtwith) - BuiltWith is a web technology profiler that uncovers the technologies powering any website. Gain actionable insights into analytics, hosting, and content management stacks for smarter research and lead generation. - [Byteforms](https://composio.dev/toolkits/byteforms) - Byteforms is an all-in-one platform for creating forms, managing submissions, and integrating data. It streamlines workflows by centralizing form data collection and automation. - [Cabinpanda](https://composio.dev/toolkits/cabinpanda) - Cabinpanda is a data collection platform for building and managing online forms. It helps streamline how you gather, organize, and analyze responses. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Abstract MCP? With a standalone Abstract MCP server, the agents and LLMs can only access a fixed set of Abstract tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Abstract 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 Abstract tools. ### Can I manage the permissions and scopes for Abstract while using Tool Router? Yes, absolutely. You can configure which Abstract 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 Abstract data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)