# How to integrate Scrapingant MCP with Pydantic AI ```json { "title": "How to integrate Scrapingant MCP with Pydantic AI", "toolkit": "Scrapingant", "toolkit_slug": "scrapingant", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/scrapingant/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/scrapingant/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:24:50.976Z" } ``` ## Introduction This guide walks you through connecting Scrapingant to Pydantic AI using the Composio tool router. By the end, you'll have a working Scrapingant agent that can extract product prices from amazon search page, convert a blog post to markdown format, get api usage stats for your account through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Scrapingant account through Composio's Scrapingant MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Scrapingant with - [OpenAI Agents SDK](https://composio.dev/toolkits/scrapingant/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/scrapingant/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/scrapingant/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/scrapingant/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/scrapingant/framework/codex) - [OpenClaw](https://composio.dev/toolkits/scrapingant/framework/openclaw) - [Hermes](https://composio.dev/toolkits/scrapingant/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/scrapingant/framework/cli) - [Google ADK](https://composio.dev/toolkits/scrapingant/framework/google-adk) - [LangChain](https://composio.dev/toolkits/scrapingant/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/scrapingant/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/scrapingant/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/scrapingant/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/scrapingant/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 Scrapingant - 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 Scrapingant 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 Scrapingant MCP server, and what's possible with it? The Scrapingant MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Scrapingant account. It provides structured and secure access to powerful web scraping tools, so your agent can extract web data, render dynamic pages, convert content to markdown, and monitor API usage on your behalf. - AI-powered data extraction: Direct your agent to pull structured data from web pages using custom AI queries—ideal for capturing tables, lists, prices, or other key information. - Dynamic web page scraping: Scrape complex sites with JavaScript rendering, proxy rotation, and support for cookies or browser emulation to handle modern web content. - Content conversion to markdown: Have your agent extract and convert page content into markdown format for clean, ready-to-use text in downstream applications or LLM pipelines. - Extended scraping with rich JSON output: Fetch detailed responses including raw HTML, headers, cookies, and other metadata to support advanced automation and analysis tasks. - API credit monitoring: Let your agent check and report on your current Scrapingant API credit usage, helping you manage quotas and avoid interruptions. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `SCRAPINGANT_EXTRACT_CONTENT_AS_MARKDOWN` | Extract Content as Markdown | This tool extracts content from a given URL and converts it into Markdown format. It is particularly useful for preparing text for Language Learning Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. It supports GET, POST, PUT, and DELETE methods. | | `SCRAPINGANT_EXTRACT_DATA_WITH_AI` | Extract Data with AI | This tool allows you to extract structured data from a web page using ScrapingAnt's AI-powered extraction capabilities. You provide a URL and an AI query (prompt) describing what data you want to extract, and the tool returns the extracted data in a structured format. It supports additional parameters for browser rendering, proxies, and cookies to handle dynamic content and localization. | | `SCRAPINGANT_GET_API_CREDITS_USAGE` | Get API Credits Usage | This tool retrieves the current API credit usage status for the authenticated ScrapingAnt account. It enables users to monitor their consumption of API credits, check their current usage against the subscription limits, and manage their API credits effectively. | | `SCRAPINGANT_SCRAPE_WEB_PAGE` | Scrape Web Page | This tool scrapes a web page using the ScrapingAnt API. It fetches the HTML content of the specified URL. Users can customize the scraping behavior by enabling a headless browser, using proxies, waiting for specific elements, executing JavaScript, passing cookies, and blocking certain resources. | | `SCRAPINGANT_SCRAPE_WEBPAGE_POST` | Scrape Webpage via POST | Tool to perform a POST request through ScrapingAnt's proxy to scrape a webpage. Use when you need to scrape pages that require POST method, such as form submissions or APIs that only accept POST requests. Data is forwarded transparently to the target web page. | | `SCRAPINGANT_SCRAPE_WEBPAGE_PUT` | Scrape Webpage with PUT | Tool to perform a PUT request through ScrapingAnt's proxy to scrape a webpage that requires PUT method. Use when the target webpage requires PUT method for data submission. Data is forwarded transparently to the target web page. | | `SCRAPINGANT_SCRAPE_WITH_EXTENDED_JSON_OUTPUT` | Scrape with Extended JSON Output | Scrapes a web page and returns comprehensive data including HTML content, plain text, cookies, HTTP headers, XHR/Fetch requests, and iframe content. This tool uses ScrapingAnt's extended endpoint which provides much richer data than standard scraping: - Full HTML and extracted plain text content - All cookies and HTTP response headers from the target page - Captured XHR/Fetch API requests made by the page (useful for finding hidden APIs) - Content from embedded iframes Best used when you need more than just the HTML - such as analyzing cookies, headers, or JavaScript API calls made by a page. For simple HTML scraping, consider using the basic scrape tool instead for lower API credit usage. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Scrapingant MCP server is an implementation of the Model Context Protocol that connects your AI agent to Scrapingant. It provides structured and secure access so your agent can perform Scrapingant 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 Scrapingant - 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 Scrapingant - MCPServerStreamableHTTP connects to the Scrapingant 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 Scrapingant 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 Scrapingant composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["scrapingant"], ) 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 Scrapingant endpoint - The agent uses GPT-5 to interpret user commands and perform Scrapingant operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent scrapingant_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[scrapingant_mcp], instructions=( "You are a Scrapingant assistant. Use Scrapingant 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 - Scrapingant 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 Scrapingant.\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 Scrapingant composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["scrapingant"], ) 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 scrapingant_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[scrapingant_mcp], instructions=( "You are a Scrapingant assistant. Use Scrapingant 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 Scrapingant.\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 Scrapingant through Composio's Tool Router. With this setup, your agent can perform real Scrapingant 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 + Scrapingant 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 Scrapingant MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/scrapingant/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/scrapingant/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/scrapingant/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/scrapingant/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/scrapingant/framework/codex) - [OpenClaw](https://composio.dev/toolkits/scrapingant/framework/openclaw) - [Hermes](https://composio.dev/toolkits/scrapingant/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/scrapingant/framework/cli) - [Google ADK](https://composio.dev/toolkits/scrapingant/framework/google-adk) - [LangChain](https://composio.dev/toolkits/scrapingant/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/scrapingant/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/scrapingant/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/scrapingant/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/scrapingant/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. - [Abstract](https://composio.dev/toolkits/abstract) - Abstract provides a suite of APIs for automating data validation and enrichment tasks. It helps developers streamline workflows and ensure data quality with minimal effort. - [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. - [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 Scrapingant MCP? With a standalone Scrapingant MCP server, the agents and LLMs can only access a fixed set of Scrapingant tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Scrapingant 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 Scrapingant tools. ### Can I manage the permissions and scopes for Scrapingant while using Tool Router? Yes, absolutely. You can configure which Scrapingant 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 Scrapingant data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)