# How to integrate Planly MCP with OpenAI Agents SDK

```json
{
  "title": "How to integrate Planly MCP with OpenAI Agents SDK",
  "toolkit": "Planly",
  "toolkit_slug": "planly",
  "framework": "OpenAI Agents SDK",
  "framework_slug": "open-ai-agents-sdk",
  "url": "https://composio.dev/toolkits/planly/framework/open-ai-agents-sdk",
  "markdown_url": "https://composio.dev/toolkits/planly/framework/open-ai-agents-sdk.md",
  "updated_at": "2026-03-29T06:45:52.303Z"
}
```

## Introduction

This guide walks you through connecting Planly to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Planly agent that can schedule a facebook post for tomorrow morning, get analytics for last week's instagram posts, list all scheduled content for this month through natural language commands.
This guide will help you understand how to give your OpenAI Agents SDK agent real control over a Planly account through Composio's Planly MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Planly with

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

The Planly MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Planly account. It provides structured and secure access so your agent can perform Planly operations on your behalf.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `PLANLY_COMPLETE_AI_PROMPT` | Complete AI Prompt | Tool to complete a text prompt using AI. Generates AI-powered text completions based on the provided prompt. Use when you need to generate creative content, complete text, or get AI suggestions for writing tasks. |
| `PLANLY_CREATE_TEAM` | Create Team | Tool to create a new team in Planly. Use when you need to create a team organization. |
| `PLANLY_DELETE_MEDIA` | Delete Media | Tool to delete one or more media files by their IDs. Use when you need to remove media files from Planly storage. |
| `PLANLY_DELETE_TEAM` | Delete Team | Tool to delete a team by its ID. Use when you need to permanently remove a team from Planly. |
| `PLANLY_EDIT_TEAM` | Edit Team | Tool to edit team details such as name in Planly. Use when you need to update an existing team's information. |
| `PLANLY_GET_AI_CREDITS` | Get AI Credits | Tool to retrieve available AI credits left in a team. Use when you need to check the remaining AI credits for a specific team. |
| `PLANLY_GET_TEAM` | Get Team | Tool to retrieve detailed information about a specific team including permissions, limits, and integrations. Use when you need to access team configuration, member counts, channel status, or integration details. |
| `PLANLY_IMPORT_MEDIA_FROM_URL` | Import Media From URL | Tool to import media from a URL to your team. Use when you need to add external media (video/mp4, image/png, image/jpeg, image/webp) to a team's media library. |
| `PLANLY_LIST_CHANNELS` | List Channels | Tool to list all social media channels connected to a team. Use when you need to retrieve channel details including name, picture, social network type, status, and scopes. |
| `PLANLY_LIST_MEDIA_FILES` | List media files | Tool to retrieve a paginated list of media files in a team. Use when you need to fetch media assets, browse uploaded files, or implement media management features with cursor-based pagination. |
| `PLANLY_LIST_SCHEDULE_GROUPS` | List Schedule Groups | Tool to retrieve a list of schedule groups for a team with comprehensive filtering and pagination. Use when you need to view scheduled posts, filter by channels, status, social networks, media type, or date range. Returns detailed information about each schedule group including individual schedules and their status. |
| `PLANLY_LIST_SCHEDULES` | List Schedules | Tool to retrieve a paginated list of schedules in a specified team. Use when you need to fetch schedules with support for pagination, custom ordering, and configurable page size. Returns schedule data with a cursor for fetching additional pages. |
| `PLANLY_LIST_TEAMS` | List Teams | Tool to retrieve all teams that the authenticated user belongs to. Use when you need to get team details including id, name, picture, role, number of users, and number of channels. |
| `PLANLY_LIST_TEAM_USERS` | List Team Users | Tool to list all users that belong to a specific team. Returns user details including id, fullname, picture, email, and role. Use when you need to retrieve the complete roster of team members. |
| `PLANLY_START_MEDIA_UPLOAD` | Start Media Upload | Tool to start the upload process for a media file. Returns a pre-signed upload URL where the file should be uploaded using a PUT request. Use when you need to prepare for uploading images or videos to Planly. |

## Supported Triggers

None listed.

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

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

mcp_url = session.mcp.url
```

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

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

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- [Brandfetch](https://composio.dev/toolkits/brandfetch) - Brandfetch is an API that delivers company logos, colors, and visual branding assets. It helps marketers and developers keep brand visuals consistent everywhere.
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- [Campayn](https://composio.dev/toolkits/campayn) - Campayn is an email marketing platform for creating, sending, and managing campaigns. It helps businesses engage contacts and grow audiences with easy-to-use tools.
- [Cardly](https://composio.dev/toolkits/cardly) - Cardly is a platform for creating and sending personalized direct mail to customers. It helps businesses break through the digital clutter by getting real engagement via physical mailboxes.
- [ClickSend](https://composio.dev/toolkits/clicksend) - ClickSend is a cloud-based SMS and email marketing platform for businesses. It streamlines communication by enabling quick message delivery and contact management.
- [Crustdata](https://composio.dev/toolkits/crustdata) - CrustData is an AI-powered data intelligence platform for real-time company and people data. It helps B2B sales teams, AI SDRs, and investors react to live business signals.
- [Curated](https://composio.dev/toolkits/curated) - Curated is a platform for collecting, curating, and publishing newsletters. It streamlines content aggregation and distribution for creators and teams.
- [Customerio](https://composio.dev/toolkits/customerio) - Customer.io is a customer engagement platform for targeted messaging across email, SMS, and push. Easily automate, segment, and track communications with your audience.
- [Cutt ly](https://composio.dev/toolkits/cutt_ly) - Cutt.ly is a URL shortening service for managing and analyzing links. Streamline your workflows with quick, trackable, and branded short URLs.
- [Demio](https://composio.dev/toolkits/demio) - Demio is webinar software built for marketers, offering both live and automated sessions with interactive features. It helps teams engage audiences and optimize lead generation through detailed analytics.

## Frequently Asked Questions

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

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

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

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

---
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