How to integrate Junglescout MCP with Pydantic AI

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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 my top products, get historical search volume for 'wireless earbuds', find keywords where my asin ranks high, list products in electronics with low competition 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.

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 & Triggers

Tools
Retrieve historical search volume data for a keywordFetches the historical search volume data for a specified keyword over a given time period.
Query the product databaseQueries the jungle scout product database to retrieve product data based on various filters.
Retrieve data for a specific keyword queryReturns data based on a specific keyword query, including search volume and competition.
Retrieve keyword data for specified asinsReturns keywords for which the queried asin(s) appear in amazon search results.
Retrieve sales estimates dataFetches sales estimates data for specified parameters.
Retrieve share of voice dataFetches share of voice data for specified keywords.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

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

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard 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.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install dependencies

bash
pip install composio pydantic-ai python-dotenv

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

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

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

Import dependencies

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()
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

Create a Tool Router Session

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")
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

Initialize the Pydantic AI Agent

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."
    ),
)
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

Build the chat interface

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()
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

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Junglescout and Pydantic AI:

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

OpenAI Agents SDK

Use Junglescout MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Junglescout MCP with Claude Agent SDK

Google ADK

Google ADK

Use Junglescout MCP with Google ADK

LangChain

LangChain

Use Junglescout MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Junglescout MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Junglescout MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Junglescout MCP with LlamaIndex

CrewAI

CrewAI

Use Junglescout MCP with CrewAI

FAQ

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.

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