# How to integrate Junglescout MCP with OpenAI Agents SDK

```json
{
  "title": "How to integrate Junglescout MCP with OpenAI Agents SDK",
  "toolkit": "Junglescout",
  "toolkit_slug": "junglescout",
  "framework": "OpenAI Agents SDK",
  "framework_slug": "open-ai-agents-sdk",
  "url": "https://composio.dev/toolkits/junglescout/framework/open-ai-agents-sdk",
  "markdown_url": "https://composio.dev/toolkits/junglescout/framework/open-ai-agents-sdk.md",
  "updated_at": "2026-05-12T10:16:35.868Z"
}
```

## Introduction

This guide walks you through connecting Junglescout to the OpenAI Agents SDK 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 OpenAI Agents SDK 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

- [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:
- Get and set up your OpenAI and Composio API keys
- Install the necessary dependencies
- Initialize Composio and create a Tool Router session for Junglescout
- Configure an AI agent that can use Junglescout as a tool
- Run a live chat session where you can ask the agent to perform Junglescout operations

## What is OpenAI Agents SDK?

The OpenAI Agents SDK is a lightweight framework for building AI agents that can use tools and maintain conversation state. It provides a simple interface for creating agents with hosted MCP tool support.
Key features include:
- Hosted MCP Tools: Connect to external services through hosted MCP endpoints
- SQLite Sessions: Persist conversation history across interactions
- Simple API: Clean interface with Agent, Runner, and tool configuration
- Streaming Support: Real-time response streaming for interactive applications

## 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:
- Composio API Key and OpenAI API Key
- Primary know-how of OpenAI Agents SDK
- A live Junglescout project
- Some knowledge of Python or Typescript

### 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).
- Go to Settings and copy your API key.

### 2. Install dependencies

Install the Composio SDK and the OpenAI Agents SDK.
```python
pip install composio_openai_agents openai-agents python-dotenv
```

```typescript
npm install @composio/openai-agents @openai/agents dotenv
```

### 3. Set up environment variables

Create a .env file and add your OpenAI and Composio API keys.
```bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com
```

### 4. Import dependencies

What's happening:
- You're importing all necessary libraries.
- The Composio and OpenAIAgentsProvider classes are imported to connect your OpenAI agent to Composio tools like Junglescout.
```python
import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession
```

```typescript
import 'dotenv/config';
import { Composio } from '@composio/core';
import { OpenAIAgentsProvider } from '@composio/openai-agents';
import { Agent, hostedMcpTool, run, OpenAIConversationsSession } from '@openai/agents';
import * as readline from 'readline';
```

### 5. Set up the Composio instance

No description provided.
```python
load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())
```

```typescript
dotenv.config();

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.USER_ID;

if (!composioApiKey) {
  throw new Error('COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key');
}
if (!userId) {
  throw new Error('USER_ID is not set');
}

// Initialize Composio
const composio = new Composio({
  apiKey: composioApiKey,
  provider: new OpenAIAgentsProvider(),
});
```

### 6. Create a Tool Router session

What is happening:
- You give the Tool Router the user id and the toolkits you want available. Here, it is only junglescout.
- The router checks the user's Junglescout connection and prepares the MCP endpoint.
- The returned session.mcp.url is the MCP URL that your agent will use to access Junglescout.
- This approach keeps things lightweight and lets the agent request Junglescout tools only when needed during the conversation.
```python
# Create a Junglescout Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["junglescout"]
)

mcp_url = session.mcp.url
```

```typescript
// Create Tool Router session for Junglescout
const session = await composio.create(userId as string, {
  toolkits: ['junglescout'],
});
const mcpUrl = session.mcp.url;
```

### 7. Configure the agent

No description provided.
```python
# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Junglescout. "
        "Help users perform Junglescout operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)
```

```typescript
// Configure agent with MCP tool
const agent = new Agent({
  name: 'Assistant',
  model: 'gpt-5',
  instructions:
    'You are a helpful assistant that can access Junglescout. Help users perform Junglescout operations through natural language.',
  tools: [
    hostedMcpTool({
      serverLabel: 'tool_router',
      serverUrl: mcpUrl,
      headers: { 'x-api-key': composioApiKey },
      requireApproval: 'never',
    }),
  ],
});
```

### 8. Start chat loop and handle conversation

No description provided.
```python
print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
```

```typescript
// Keep conversation state across turns
const conversationSession = new OpenAIConversationsSession();

// Simple CLI
const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: 'You: ',
});

console.log('\nComposio Tool Router session created.');
console.log('\nChat started. Type your requests below.');
console.log("Commands: 'exit', 'quit', or 'q' to end\n");

try {
  const first = await run(agent, 'What can you help me with?', { session: conversationSession });
  console.log(`Assistant: ${first.finalOutput}\n`);
} catch (e) {
  console.error('Error:', e instanceof Error ? e.message : e, '\n');
}

rl.prompt();

rl.on('line', async (userInput) => {
  const text = userInput.trim();

  if (['exit', 'quit', 'q'].includes(text.toLowerCase())) {
    console.log('Goodbye!');
    rl.close();
    process.exit(0);
  }

  if (!text) {
    rl.prompt();
    return;
  }

  try {
    const result = await run(agent, text, { session: conversationSession });
    console.log(`\nAssistant: ${result.finalOutput}\n`);
  } catch (e) {
    console.error('Error:', e instanceof Error ? e.message : e, '\n');
  }

  rl.prompt();
});

rl.on('close', () => {
  console.log('\n👋 Session ended.');
  process.exit(0);
});
```

## Complete Code

```python
import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["junglescout"]
)
mcp_url = session.mcp.url

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Junglescout. "
        "Help users perform Junglescout operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
```

```typescript
import 'dotenv/config';
import { Composio } from '@composio/core';
import { OpenAIAgentsProvider } from '@composio/openai-agents';
import { Agent, hostedMcpTool, run, OpenAIConversationsSession } from '@openai/agents';
import * as readline from 'readline';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.USER_ID;

if (!composioApiKey) {
  throw new Error('COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key');
}
if (!userId) {
  throw new Error('USER_ID is not set');
}

// Initialize Composio
const composio = new Composio({
  apiKey: composioApiKey,
  provider: new OpenAIAgentsProvider(),
});

async function main() {
  // Create Tool Router session
  const session = await composio.create(userId as string, {
    toolkits: ['junglescout'],
  });
  const mcpUrl = session.mcp.url;

  // Configure agent with MCP tool
  const agent = new Agent({
    name: 'Assistant',
    model: 'gpt-5',
    instructions:
      'You are a helpful assistant that can access Junglescout. Help users perform Junglescout operations through natural language.',
    tools: [
      hostedMcpTool({
        serverLabel: 'tool_router',
        serverUrl: mcpUrl,
        headers: { 'x-api-key': composioApiKey },
        requireApproval: 'never',
      }),
    ],
  });

  // Keep conversation state across turns
  const conversationSession = new OpenAIConversationsSession();

  // Simple CLI
  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: ',
  });

  console.log('\nComposio Tool Router session created.');
  console.log('\nChat started. Type your requests below.');
  console.log("Commands: 'exit', 'quit', or 'q' to end\n");

  try {
    const first = await run(agent, 'What can you help me with?', { session: conversationSession });
    console.log(`Assistant: ${first.finalOutput}\n`);
  } catch (e) {
    console.error('Error:', e instanceof Error ? e.message : e, '\n');
  }

  rl.prompt();

  rl.on('line', async (userInput) => {
    const text = userInput.trim();

    if (['exit', 'quit', 'q'].includes(text.toLowerCase())) {
      console.log('Goodbye!');
      rl.close();
      process.exit(0);
    }

    if (!text) {
      rl.prompt();
      return;
    }

    try {
      const result = await run(agent, text, { session: conversationSession });
      console.log(`\nAssistant: ${result.finalOutput}\n`);
    } catch (e) {
      console.error('Error:', e instanceof Error ? e.message : e, '\n');
    }

    rl.prompt();
  });

  rl.on('close', () => {
    console.log('\nSession ended.');
    process.exit(0);
  });
}

main().catch((err) => {
  console.error('Fatal error:', err);
  process.exit(1);
});
```

## Conclusion

This was a starter code for integrating Junglescout MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Junglescout.
Key features:
- Hosted MCP tool integration through Composio's Tool Router
- SQLite session persistence for conversation history
- Simple async chat loop for interactive testing
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

## How to build Junglescout MCP Agent with another framework

- [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

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- [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.
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- [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.
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- [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 OpenAI Agents SDK?

Yes, you can. OpenAI Agents SDK 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)