# How to integrate Piloterr MCP with OpenAI Agents SDK

```json
{
  "title": "How to integrate Piloterr MCP with OpenAI Agents SDK",
  "toolkit": "Piloterr",
  "toolkit_slug": "piloterr",
  "framework": "OpenAI Agents SDK",
  "framework_slug": "open-ai-agents-sdk",
  "url": "https://composio.dev/toolkits/piloterr/framework/open-ai-agents-sdk",
  "markdown_url": "https://composio.dev/toolkits/piloterr/framework/open-ai-agents-sdk.md",
  "updated_at": "2026-05-12T10:21:58.411Z"
}
```

## Introduction

This guide walks you through connecting Piloterr to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Piloterr agent that can find trending laptops on bestbuy today, get full details for auchan product id 12345, search for organic snacks on auchan through natural language commands.
This guide will help you understand how to give your OpenAI Agents SDK agent real control over a Piloterr account through Composio's Piloterr MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Piloterr with

- [Claude Agent SDK](https://composio.dev/toolkits/piloterr/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/piloterr/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/piloterr/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/piloterr/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/piloterr/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/piloterr/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/piloterr/framework/cli)
- [Google ADK](https://composio.dev/toolkits/piloterr/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/piloterr/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/piloterr/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/piloterr/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/piloterr/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/piloterr/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 Piloterr
- Configure an AI agent that can use Piloterr as a tool
- Run a live chat session where you can ask the agent to perform Piloterr 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 Piloterr MCP server, and what's possible with it?

The Piloterr MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Piloterr account. It provides structured and secure access to a suite of powerful web data extraction APIs, so your agent can perform actions like searching products, retrieving detailed product info, and monitoring account usage on your behalf.
- Product search on retail platforms: Direct your agent to search for products on sites like Auchan or Bestbuy by keyword, category, or filters, and receive curated product listings.
- Retrieve detailed product information: Ask your agent to fetch comprehensive product details—including company info—using specific product IDs from supported platforms.
- Monitor account usage and credits: Let your agent check your Piloterr account usage and remaining credits, so you always know how much data access you have left.
- Automated product data workflows: Enable your agent to seamlessly combine product search and retrieval, powering advanced e-commerce, analytics, or market research tasks.
- Multi-platform product integration: Effortlessly access and aggregate product data from multiple online stores to inform business decisions or drive automation.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `PILOTERR_AUCHAN_PRODUCT` | Auchan Product | Tool to retrieve detailed Auchan product information. Use when you have an Auchan product URL. |
| `PILOTERR_AUCHAN_SEARCH` | Auchan Product Search | Tool to search for products on Auchan by query or search URL. Use when you need product listings and details from Auchan. |
| `PILOTERR_GET_PRODUCT_INFO` | Piloterr Get Product Info | Tool to retrieve detailed product information from G2. Use when you need full product details including ratings, reviews, pricing, and company info. Accepts a product name or G2 URL as query. |
| `PILOTERR_LIST_CHEWY_PRODUCTS` | List Chewy Products | Tool to find Chewy product categories by search query. Use when you need to retrieve Chewy product URLs and details based on product ID or title. |
| `PILOTERR_LIST_ELECLERC_STORES` | List E.Leclerc Stores | Tool to find E.Leclerc store locations by search query. Use when you need to locate E.Leclerc stores in a specific area or city. |
| `PILOTERR_LIST_GOOGLE_COUNTRIES` | List Google Countries | Tool to list available Google search countries. Use when you need to find country codes for Google search localization. |
| `PILOTERR_LIST_GOOGLE_LANGUAGES` | List Google Languages | Tool to list available Google search languages filtered by query. Use when you need to find specific language codes for Google searches. |
| `PILOTERR_LIST_GOOGLE_LOCATIONS` | List Google Locations | Tool to list available Google search locations via Piloterr API. Use when you need to find location identifiers for geographically-targeted searches. |
| `PILOTERR_LIST_LEROY_MERLIN_PRODUCTS` | List Leroy Merlin Products | Tool to list Leroy Merlin product categories. Use when you need to search for Leroy Merlin products by name or identifier. |
| `PILOTERR_LIST_LINKED_IN_INDUSTRIES` | List LinkedIn Industries | Tool to list LinkedIn industry codes from Piloterr API. Use when you need to find LinkedIn industry identifiers for targeting or classification. |
| `PILOTERR_SEARCH` | Piloterr Google Search | Tool to perform Google web search via Piloterr API. Use when you need to search the web for information using Google. |
| `PILOTERR_USAGE_GET` | Get Usage | Tool to get usage information and remaining credits for your Piloterr account. Use when you need to check account usage and credit details. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Piloterr MCP server is an implementation of the Model Context Protocol that connects your AI agent to Piloterr. It provides structured and secure access so your agent can perform Piloterr 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 Piloterr 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 Piloterr.
```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 piloterr.
- The router checks the user's Piloterr connection and prepares the MCP endpoint.
- The returned session.mcp.url is the MCP URL that your agent will use to access Piloterr.
- This approach keeps things lightweight and lets the agent request Piloterr tools only when needed during the conversation.
```python
# Create a Piloterr Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["piloterr"]
)

mcp_url = session.mcp.url
```

```typescript
// Create Tool Router session for Piloterr
const session = await composio.create(userId as string, {
  toolkits: ['piloterr'],
});
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 Piloterr. "
        "Help users perform Piloterr 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 Piloterr. Help users perform Piloterr 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=["piloterr"]
)
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 Piloterr. "
        "Help users perform Piloterr 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: ['piloterr'],
  });
  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 Piloterr. Help users perform Piloterr 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 Piloterr MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Piloterr.
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 Piloterr MCP Agent with another framework

- [Claude Agent SDK](https://composio.dev/toolkits/piloterr/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/piloterr/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/piloterr/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/piloterr/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/piloterr/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/piloterr/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/piloterr/framework/cli)
- [Google ADK](https://composio.dev/toolkits/piloterr/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/piloterr/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/piloterr/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/piloterr/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/piloterr/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/piloterr/framework/crew-ai)

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- [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.
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- [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.
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- [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 Piloterr MCP?

With a standalone Piloterr MCP server, the agents and LLMs can only access a fixed set of Piloterr tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Piloterr 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 Piloterr tools.

### Can I manage the permissions and scopes for Piloterr while using Tool Router?

Yes, absolutely. You can configure which Piloterr 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 Piloterr data and credentials are handled as safely as possible.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)