# How to integrate Junglescout MCP with Pydantic AI ```json { "title": "How to integrate Junglescout MCP with Pydantic AI", "toolkit": "Junglescout", "toolkit_slug": "junglescout", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/junglescout/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/junglescout/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:16:35.868Z" } ``` ## Introduction This guide walks you through connecting Junglescout to Pydantic AI using the Composio tool router. By the end, you'll have a working Junglescout agent that can show sales estimates for your top products, get historical search volume for 'wireless earbuds', find keywords where your asin ranks high through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Junglescout account through Composio's Junglescout MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Junglescout with - [OpenAI Agents SDK](https://composio.dev/toolkits/junglescout/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/junglescout/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/junglescout/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/junglescout/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/junglescout/framework/codex) - [OpenClaw](https://composio.dev/toolkits/junglescout/framework/openclaw) - [Hermes](https://composio.dev/toolkits/junglescout/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/junglescout/framework/cli) - [Google ADK](https://composio.dev/toolkits/junglescout/framework/google-adk) - [LangChain](https://composio.dev/toolkits/junglescout/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/junglescout/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/junglescout/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/junglescout/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/junglescout/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 Junglescout - 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 Junglescout 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 Junglescout MCP server, and what's possible with it? The Junglescout MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Junglescout account. It provides structured and secure access to Amazon product insights, so your agent can perform actions like product research, sales estimation, keyword analysis, and competitive tracking on your behalf. - Comprehensive product database queries: Direct your agent to search Jungle Scout’s product database using specific filters, so you can quickly identify profitable Amazon products based on criteria like price, rank, sales, reviews, and more. - Historical keyword search analysis: Retrieve detailed historical search volume data for any keyword, letting your agent uncover trends and demand patterns to guide your product or marketing strategy. - ASIN-based keyword discovery: Have your agent find which keywords a set of ASINs rank for on Amazon, helping you analyze competitors or optimize your own listings. - Sales estimates and revenue projections: Effortlessly ask your agent to fetch sales estimates for specific products or niches, making inventory planning and revenue forecasting a breeze. - Share of voice and competitive analysis: Let your agent pull share of voice data for your target keywords, giving you insights into brand visibility and the competitive landscape in your market. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `JUNGLESCOUT_KEYWORD_HISTORICAL_VOLUME` | Retrieve historical search volume data for a keyword | Fetches the historical search volume data for a specified keyword over a given time period. | | `JUNGLESCOUT_QUERY_THE_PRODUCT_DATABASE` | Query the product database | Queries the Jungle Scout product database to retrieve product data based on various filters. Compatible parameters include marketplace, sort, page_size, product_tiers, seller_types, categories, exclude_top_brands, exclude_unavailable_products, min_price, max_price, min_net, max_net, min_rank, max_rank, min_sales, max_sales, min_revenue, max_revenue, min_reviews, max_reviews, min_rating, max_rating, min_weight, max_weight, min_sellers, max_sellers, min_lqs, max_lqs, min_updated_at, and max_updated_at. | | `JUNGLESCOUT_RETRIEVE_DATA_FOR_A_SPECIFIC_KEYWORD_QUERY` | Retrieve data for a specific keyword query | Returns data based on a specific keyword query, including search volume and competition. | | `JUNGLESCOUT_RETRIEVE_KEYWORD_DATA_FOR_SPECIFIED_ASINS` | Retrieve keyword data for specified asins | Returns keywords for which the queried ASIN(s) appear in Amazon search results. For a given keyword, Jungle Scout collects up to 3 pages of Amazon keyword search results. Query up to 10 ASINs at a time. Apply filters to narrow search results. | | `JUNGLESCOUT_RETRIEVE_SALES_ESTIMATES_DATA` | Retrieve sales estimates data | Fetches sales estimates data for specified parameters. | | `JUNGLESCOUT_RETRIEVE_SHARE_OF_VOICE_DATA` | Retrieve share of voice data | Fetches share of voice data for specified keywords. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Junglescout MCP server is an implementation of the Model Context Protocol that connects your AI agent to Junglescout. It provides structured and secure access so your agent can perform Junglescout 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 Junglescout - 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 Junglescout - MCPServerStreamableHTTP connects to the Junglescout 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 Junglescout 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 Junglescout composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["junglescout"], ) 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 Junglescout endpoint - The agent uses GPT-5 to interpret user commands and perform Junglescout operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent junglescout_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[junglescout_mcp], instructions=( "You are a Junglescout assistant. Use Junglescout 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 - Junglescout 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 Junglescout.\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 Junglescout composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["junglescout"], ) 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 junglescout_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[junglescout_mcp], instructions=( "You are a Junglescout assistant. Use Junglescout 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 Junglescout.\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 Junglescout through Composio's Tool Router. With this setup, your agent can perform real Junglescout 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 + Junglescout 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 Junglescout MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/junglescout/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/junglescout/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/junglescout/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/junglescout/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/junglescout/framework/codex) - [OpenClaw](https://composio.dev/toolkits/junglescout/framework/openclaw) - [Hermes](https://composio.dev/toolkits/junglescout/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/junglescout/framework/cli) - [Google ADK](https://composio.dev/toolkits/junglescout/framework/google-adk) - [LangChain](https://composio.dev/toolkits/junglescout/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/junglescout/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/junglescout/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/junglescout/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/junglescout/framework/crew-ai) ## Related Toolkits - [Addresszen](https://composio.dev/toolkits/addresszen) - Addresszen is a real-time address autocomplete and verification service. It helps capture accurate, deliverable addresses with instant suggestions and validation. - [Asin data api](https://composio.dev/toolkits/asin_data_api) - Asin data api gives you detailed, real-time product data from Amazon, including price, rank, and reviews. Perfect for e-commerce pros and data-driven marketers who need instant marketplace insights. - [Baselinker](https://composio.dev/toolkits/baselinker) - BaseLinker is an all-in-one e-commerce management platform connecting stores, marketplaces, carriers, and more. It streamlines order processing, inventory control, and automates your sales operations. - [Bestbuy](https://composio.dev/toolkits/bestbuy) - Best Buy is a leading retailer offering APIs for product, store, and recommendation data. Instantly access up-to-date retail insights for smarter shopping and decision-making. - [Btcpay server](https://composio.dev/toolkits/btcpay_server) - BTCPay Server is a free, open-source, self-hosted Bitcoin payment processor. It lets merchants accept Bitcoin payments directly, cutting out middlemen and boosting privacy. - [Cdr platform](https://composio.dev/toolkits/cdr_platform) - Cdr platform is an API for purchasing carbon dioxide removal services. It enables businesses to offset emissions by accessing verified carbon removal projects. - [Cloudcart](https://composio.dev/toolkits/cloudcart) - CloudCart is an e-commerce platform for building and managing online stores. It helps businesses streamline product listings, orders, and customer engagement. - [Countdown api](https://composio.dev/toolkits/countdown_api) - Countdown API gives you real-time, structured eBay product data, reviews, and seller feedback. Perfect for powering price monitoring, product research, or marketplace analytics workflows. - [Dpd2](https://composio.dev/toolkits/dpd2) - Dpd2 is a robust email management platform for handling, sorting, and automating email workflows. Streamline your communications and boost productivity with advanced sorting, labeling, and response tools. - [Finerworks](https://composio.dev/toolkits/finerworks) - FinerWorks is an online platform for fine art and photo printing services. Artists and photographers use it to order custom prints and manage print inventory efficiently. - [Fingertip](https://composio.dev/toolkits/fingertip) - Fingertip is a business management platform for selling, booking, and customer engagement—all from a single link. It helps businesses streamline operations and connect with customers across social channels. - [Fraudlabs pro](https://composio.dev/toolkits/fraudlabs_pro) - FraudLabs Pro is an online payment fraud detection service for e-commerce and merchants. It helps minimize chargebacks and revenue loss by detecting and preventing fraudulent transactions. - [Gift up](https://composio.dev/toolkits/gift_up) - Gift Up! is a digital platform for selling, managing, and redeeming gift cards online. It streamlines promotions and gift card transactions for businesses and their customers. - [Goody](https://composio.dev/toolkits/goody) - Goody is a gifting platform that lets users send gifts and physical products without handling logistics. It streamlines gifting by managing delivery, fulfillment, and recipient experience. - [Gumroad](https://composio.dev/toolkits/gumroad) - Gumroad is a platform for selling digital products, physical goods, and memberships with a simple checkout and marketing tools. It streamlines creator payouts and helps you grow your audience effortlessly. - [Instacart](https://composio.dev/toolkits/instacart) - Instacart is an online grocery delivery and pickup service platform. It lets you discover local retailers and create shoppable lists and recipes with ease. - [Ko fi](https://composio.dev/toolkits/ko_fi) - Ko-fi is a platform that lets creators receive donations, memberships, and sales from fans. It helps creators monetize their work and grow their audience with minimal friction. - [Lemon squeezy](https://composio.dev/toolkits/lemon_squeezy) - Lemon Squeezy is a payments and subscription platform built for software companies. It makes managing payments, taxes, and customer subscriptions effortless. - [Loyverse](https://composio.dev/toolkits/loyverse) - Loyverse is a point-of-sale (POS) platform for small businesses, offering tools for sales, inventory, and customer loyalty. It helps streamline retail operations and boost customer engagement. - [Memberstack](https://composio.dev/toolkits/memberstack) - Memberstack lets you add user authentication, payments, and member management to your website—no backend code required. Easily manage your site's members and subscriptions from a single platform. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Junglescout MCP? With a standalone Junglescout MCP server, the agents and LLMs can only access a fixed set of Junglescout tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Junglescout 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 Junglescout tools. ### Can I manage the permissions and scopes for Junglescout while using Tool Router? Yes, absolutely. You can configure which Junglescout 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 Junglescout data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)