# How to integrate Modelry MCP with OpenAI Agents SDK

```json
{
  "title": "How to integrate Modelry MCP with OpenAI Agents SDK",
  "toolkit": "Modelry",
  "toolkit_slug": "modelry",
  "framework": "OpenAI Agents SDK",
  "framework_slug": "open-ai-agents-sdk",
  "url": "https://composio.dev/toolkits/modelry/framework/open-ai-agents-sdk",
  "markdown_url": "https://composio.dev/toolkits/modelry/framework/open-ai-agents-sdk.md",
  "updated_at": "2026-05-12T10:19:19.161Z"
}
```

## Introduction

This guide walks you through connecting Modelry to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Modelry agent that can list all modeling requests in your workspace, create a new workspace for your models, get details for a specific embed through natural language commands.
This guide will help you understand how to give your OpenAI Agents SDK agent real control over a Modelry account through Composio's Modelry MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Modelry with

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

The Modelry MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Modelry account. It provides structured and secure access to your machine learning model management, so your agent can perform actions like listing modeling requests, creating workspaces, retrieving embed details, and managing products on your behalf.
- Workspace management: Easily create new workspaces or fetch details about existing ones to keep your projects organized and separated.
- Embed and product operations: List all available embeds, get detailed information, or delete embeds and products as needed for smooth deployment and maintenance.
- Repository handling: Retrieve details of product repositories or remove repositories you no longer need—all with structured agent commands.
- Modeling request tracking: Quickly list all 3D modeling requests tied to your account to monitor progress and manage workflows efficiently.
- Secure automated actions: Let your agent handle repetitive or administrative model management tasks securely, saving you time and effort.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `MODELRY_CREATE_WORKSPACE` | Create Workspace | Create a new workspace or return an existing one with the same name. Workspaces are used to organize products and embeds in Modelry. If workspace creation is not supported by the API, this tool will return an existing workspace matching the requested name. |
| `MODELRY_DELETE_EMBED` | Delete Modelry Embed | Tool to delete an embed. Tries multiple common endpoint patterns and treats 404 as idempotent success after exhausting candidates (embed already deleted or not found). |
| `MODELRY_DELETE_PRODUCT` | Delete Modelry Product | Permanently deletes a product from Modelry by its ID. Use this action to remove a product that is no longer needed. This operation is idempotent - deleting an already-deleted product will succeed without error. Prerequisites: - Obtain the product ID using MODELRY_LIST_PRODUCTS first - Ensure you have delete permissions for the product WARNING: This action is destructive and cannot be undone. |
| `MODELRY_DELETE_PRODUCT_REPOSITORY` | Delete Product Repository | Permanently delete a product repository from Modelry. This action is idempotent - deleting a non-existent repository returns success. Use the list product repositories action first to get valid repository IDs. |
| `MODELRY_DELETE_WORKSPACE` | Delete Modelry Workspace | Permanently deletes a Modelry workspace. This action is idempotent - deleting a non-existent workspace will return success. Use the list workspaces action first to get valid workspace IDs. WARNING: This is a destructive action that cannot be undone. |
| `MODELRY_GET_EMBED` | Get Embed | Retrieve details of a specific Modelry embed (3D viewer or AR experience for eCommerce). Use MODELRY_LIST_EMBEDS first to obtain valid embed IDs. Returns embed metadata including status, workspace, and configuration details. |
| `MODELRY_GET_WORKSPACE` | Get Workspace | Retrieves details for a specific Modelry workspace by its ID or name. The workspace ID can be obtained from the List Workspaces action. This action fetches all workspaces and returns the matching one. |
| `MODELRY_LIST_EMBEDS` | List Embeds | List embeds in Modelry. Embeds are 3D viewer/AR embed codes for products. Use to retrieve embed IDs for downstream actions (e.g., MODELRY_GET_EMBED, MODELRY_DELETE_EMBED). Returns empty list if no embeds exist. Supports pagination and optional workspace filtering. |
| `MODELRY_LIST_MODELING_REQUESTS` | List Modeling Requests | List all 3D modeling requests in a workspace. Requires workspace_id to scope the request. Returns modeling requests with their status and metadata. |
| `MODELRY_LIST_PRODUCT_REPOSITORIES` | List Product Repositories | Tool to list all product repositories in a workspace. Use after confirming the workspace ID. |
| `MODELRY_LIST_PRODUCTS` | List Modelry Products | List all products in Modelry. Returns paginated product data including IDs, names, and metadata. Use this to retrieve product IDs needed for other product-related actions like delete or get details. Optionally scope to a specific workspace using workspace_id parameter. |
| `MODELRY_LIST_WORKSPACES` | List Modelry Workspaces | Tool to list all workspaces in Modelry. Use when you need to retrieve available workspaces after authenticating. |
| `MODELRY_ORDER_MODELING_SERVICE` | Order Modeling Service | Tool to place an order for 3D modeling services. Use when workspace and product IDs are known and modeling specifications are ready. |
| `MODELRY_TRACK_MODELING_PROGRESS` | Track Modeling Progress | Tool to track the progress of a 3D modeling request. Use after initiating a modeling job to poll current status and completion percentage. |

## Supported Triggers

None listed.

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

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

mcp_url = session.mcp.url
```

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

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

## Related Toolkits

- [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed.
- [Composio search](https://composio.dev/toolkits/composio_search) - Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service.
- [Perplexityai](https://composio.dev/toolkits/perplexityai) - Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows.
- [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow.
- [Ai ml api](https://composio.dev/toolkits/ai_ml_api) - Ai ml api is a suite of AI/ML models for natural language and image tasks. It provides fast, scalable access to advanced AI capabilities for your apps and workflows.
- [Aivoov](https://composio.dev/toolkits/aivoov) - Aivoov is an AI-powered text-to-speech platform offering 1,000+ voices in over 150 languages. Instantly turn written content into natural, human-like audio for any application.
- [All images ai](https://composio.dev/toolkits/all_images_ai) - All-Images.ai is an AI-powered image generation and management platform. It helps you create, search, and organize images effortlessly with advanced AI capabilities.
- [Anthropic administrator](https://composio.dev/toolkits/anthropic_administrator) - Anthropic administrator is an API for managing Anthropic organizational resources like members, workspaces, and API keys. It helps you automate admin tasks and streamline resource management across your Anthropic organization.
- [Api labz](https://composio.dev/toolkits/api_labz) - Api labz is a platform offering a suite of AI-driven APIs and workflow tools. It helps developers automate tasks and build smarter, more efficient applications.
- [Apipie ai](https://composio.dev/toolkits/apipie_ai) - Apipie ai is an AI model aggregator offering a single API for accessing top AI models from multiple providers. It helps developers build cost-efficient, latency-optimized AI solutions without juggling multiple integrations.
- [Astica ai](https://composio.dev/toolkits/astica_ai) - Astica ai provides APIs for computer vision, NLP, and voice synthesis. Integrate advanced AI features into your app with a single API key.
- [Bigml](https://composio.dev/toolkits/bigml) - BigML is a machine learning platform that lets you build, train, and deploy predictive models from your data. Its intuitive interface and robust API make machine learning accessible and efficient.
- [Botbaba](https://composio.dev/toolkits/botbaba) - Botbaba is a platform for building, managing, and deploying conversational AI chatbots across messaging channels. It streamlines chatbot automation, making it easier to integrate AI into customer interactions.
- [Botpress](https://composio.dev/toolkits/botpress) - Botpress is an open-source platform for building, deploying, and managing chatbots. It helps teams automate conversations and deliver rich, interactive messaging experiences.
- [Chatbotkit](https://composio.dev/toolkits/chatbotkit) - Chatbotkit is a platform for building and managing AI-powered chatbots using robust APIs and SDKs. It lets you easily add conversational AI to your apps for better user engagement.
- [Cody](https://composio.dev/toolkits/cody) - Cody is an AI assistant built for businesses, trained on your company's knowledge and data. It delivers instant answers and insights, tailored for your team.
- [Context7 MCP](https://composio.dev/toolkits/context7_mcp) - Context7 MCP delivers live, version-specific code docs and examples right from the source. It helps developers and AI agents instantly retrieve authoritative programming info—no more out-of-date docs.
- [Customgpt](https://composio.dev/toolkits/customgpt) - CustomGPT.ai lets you build and deploy chatbots tailored to your own data and business needs. Get precise and context-aware AI conversations without writing code.
- [Datarobot](https://composio.dev/toolkits/datarobot) - Datarobot is a machine learning platform that automates model development, deployment, and monitoring. It empowers organizations to quickly gain predictive insights from large datasets.
- [Deepgram](https://composio.dev/toolkits/deepgram) - Deepgram is an AI-powered speech recognition platform for accurate audio transcription and understanding. It enables fast, scalable speech-to-text with advanced audio intelligence features.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Modelry MCP?

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

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

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

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