# How to integrate Apipie ai MCP with OpenAI Agents SDK

```json
{
  "title": "How to integrate Apipie ai MCP with OpenAI Agents SDK",
  "toolkit": "Apipie ai",
  "toolkit_slug": "apipie_ai",
  "framework": "OpenAI Agents SDK",
  "framework_slug": "open-ai-agents-sdk",
  "url": "https://composio.dev/toolkits/apipie_ai/framework/open-ai-agents-sdk",
  "markdown_url": "https://composio.dev/toolkits/apipie_ai/framework/open-ai-agents-sdk.md",
  "updated_at": "2026-05-12T10:01:39.030Z"
}
```

## Introduction

This guide walks you through connecting Apipie ai to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Apipie ai agent that can show all available ai models for text tasks, delete unused vectors from your collection, list country restrictions for gpt-4 models through natural language commands.
This guide will help you understand how to give your OpenAI Agents SDK agent real control over a Apipie ai account through Composio's Apipie ai MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Apipie ai with

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

The Apipie ai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Apipie ai account. It provides structured and secure access to a wide range of AI models and vector operations, so your agent can perform actions like listing models, checking restrictions, managing vectors, and analyzing usage history on your behalf.
- Model discovery and selection: Instantly fetch a comprehensive list of available AI models, filter by type or provider, and stay up-to-date with the latest model options.
- Model restriction checks: Retrieve country-specific deployment restrictions, helping you ensure compliance and make informed choices before launching models in new regions.
- Vector data management: Effortlessly delete entire vector collections or specific vectors, enabling your agent to keep your data storage clean and up to date.
- Usage analytics and auditing: Access historical API query logs—see latency, token usage, costs, and source IPs—so you can monitor performance, manage expenses, and audit activity anytime.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `APIPIE_AI_ANONYMIZE_TEXT` | Anonymize Sensitive Text | Anonymize sensitive entities (PII) in text for data privacy and compliance. Use this tool to detect and replace personally identifiable information like names, phone numbers, locations, and other sensitive data with truncated SHA-256 hashes. Returns both the anonymized text and mappings showing what was replaced. |
| `APIPIE_AI_CREATE_VECTOR_COLLECTION` | Create Vector Collection | Create a new vector collection (Pinecone-style index and namespace combined) in APIpie. Use this when you need to set up a new vector database for storing embeddings with a specific dimension. The dimension must match the embedding model you'll use (e.g., 1536 for OpenAI text-embedding-ada-002). |
| `APIPIE_AI_DELETE_STATE` | Delete state | Tool to delete state settings from APIpie. Without query parameter deletes app-level state; with query parameter deletes specific user state. Use after configuring state to remove unwanted state records or reset configuration. |
| `APIPIE_AI_DELETE_VECTORS` | Delete Vectors | Delete vectors from a vector collection in APIpie. Use this tool to: - Delete ALL vectors in a collection: set delete_all=True (requires credits) - Delete specific vectors: set delete_all=False and provide a list of vector IDs Note: Deleting by metadata filter is not currently supported - you must specify vector IDs. |
| `APIPIE_AI_GET_DETAILED_MODELS` | Get Detailed Models | Fetch detailed information about available AI models including pricing, capabilities, and specifications. Use when you need comprehensive model data with pricing rates, token limits, modality support, and benchmark scores. |
| `APIPIE_AI_GET_QUERY_HISTORY` | Get query history | Tool to retrieve historic API usage logs including latency, token counts, costs, and source IP. Use after authenticating to analyze past queries for cost management, performance monitoring, or auditing. |
| `APIPIE_AI_GET_STATE` | Get state | Tool to retrieve current state settings including user preferences, memory configuration, and routing settings. Use when you need to check or audit the current configuration for an app or specific user. |
| `APIPIE_AI_LIST_MODELS` | List AI Models | Fetch a list of available AI models from APIPie. Use this tool when you need: - Up-to-date model listings with filtering by type, subtype, or provider - Voice model listings (set voices=true) - Country restriction information (set restrictions=true) Returns models with pricing, latency, availability, and capability information. |
| `APIPIE_AI_LIST_VECTOR_COLLECTIONS` | List Vector Collections | Tool to retrieve a list of all vector collections under your account. Use when you need to view available collections before performing vector operations like querying, upserting, or deleting vectors. |
| `APIPIE_AI_PARSE_DOCUMENT` | Parse Document | Tool to parse document content and metadata using Apache Tika. Extract text and metadata from various document formats (PDF, DOCX, TXT, etc.). Use when you need to extract readable text or metadata from uploaded documents. |
| `APIPIE_AI_TRANSCRIBE_AUDIO` | Transcribe audio to text | Tool to transcribe audio files to text using AI speech-to-text models like Whisper. Use when you need to convert spoken audio into written text. Supports multiple models and output formats. |
| `APIPIE_AI_UPDATE_STATE` | Update State Settings | Tool to create or update state settings in APIpie, including configurations, deletions, and feature toggling at app or user levels. Use when you need to manage persistent state for AI completions, memory, routing, or other APIpie features. |
| `APIPIE_AI_UPLOAD_FILE` | Upload File | Upload a file to APIPie and retrieve a temporary URL. Use when you need to upload an image file and get a shareable URL. Supports image formats (.png, .jpg, .jpeg, .svg, .gif, .bmp, .tif, .tiff, .webp) with a maximum size limit of 5MB. |

## Supported Triggers

None listed.

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

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

mcp_url = session.mcp.url
```

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

- [Claude Agent SDK](https://composio.dev/toolkits/apipie_ai/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/apipie_ai/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/apipie_ai/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/apipie_ai/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/apipie_ai/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/apipie_ai/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/apipie_ai/framework/cli)
- [Google ADK](https://composio.dev/toolkits/apipie_ai/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/apipie_ai/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/apipie_ai/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/apipie_ai/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/apipie_ai/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/apipie_ai/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.
- [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.
- [DeepImage](https://composio.dev/toolkits/deepimage) - DeepImage is an AI-powered image enhancer and upscaler. Get higher-quality images with just a few clicks.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Apipie ai MCP?

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

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

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

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