# How to integrate Feathery MCP with OpenAI Agents SDK

```json
{
  "title": "How to integrate Feathery MCP with OpenAI Agents SDK",
  "toolkit": "Feathery",
  "toolkit_slug": "feathery",
  "framework": "OpenAI Agents SDK",
  "framework_slug": "open-ai-agents-sdk",
  "url": "https://composio.dev/toolkits/feathery/framework/open-ai-agents-sdk",
  "markdown_url": "https://composio.dev/toolkits/feathery/framework/open-ai-agents-sdk.md",
  "updated_at": "2026-05-06T08:11:18.407Z"
}
```

## Introduction

This guide walks you through connecting Feathery to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Feathery agent that can list all forms created this month, get schema for user registration form, fill document template with client details through natural language commands.
This guide will help you understand how to give your OpenAI Agents SDK agent real control over a Feathery account through Composio's Feathery MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Feathery with

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

The Feathery MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Feathery account. It provides structured and secure access to your forms and workflow data, so your agent can perform actions like creating hidden fields, retrieving form schemas, listing documents, and managing account settings on your behalf.
- Form discovery and management: Let your agent list all existing forms, retrieve specific form schemas, or permanently remove forms as needed.
- Document automation and signing: Automatically fill or sign document templates and track generated document envelopes for streamlined data processing.
- Hidden field configuration: Create new hidden fields or list all hidden fields within your forms for advanced workflow logic and data capture.
- Account and team management: Fetch detailed account info, update user roles and permissions, and manage your Feathery team's access seamlessly.
- Integration troubleshooting: List recent API connector errors tied to specific forms, making it easier to debug and maintain your integrations.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `FEATHERY_ACCOUNT_EDIT` | Edit Feathery Account | Tool to edit an existing account’s role and permissions. Use when modifying account settings after confirming identity. |
| `FEATHERY_ACCOUNT_GET_INFO` | Get Account Info | Tool to get your Feathery team name and list of accounts. Use when you need to fetch your team's account info. |
| `FEATHERY_DOCUMENT_FILL_TEMPLATE` | Fill or sign document template | Tool to fill or sign a Feathery document template. Use when you need to generate a completed or signed document file after mapping template fields. |
| `FEATHERY_DOCUMENT_LIST_ENVELOPES` | List Document Envelopes | Tool to list generated document envelopes by document or user ID. Use when you need to retrieve envelope records for auditing or tracking. |
| `FEATHERY_FORM_CREATE_HIDDEN_FIELD` | Create hidden field | Tool to create a new hidden field in a form. Use when you need to add a non-visible field after confirming you have a unique field_id. |
| `FEATHERY_FORM_DELETE` | Delete Form | Tool to delete an existing form. Use when you need to permanently remove a form after confirmation. |
| `FEATHERY_FORM_GET_SCHEMA` | Get form schema | Tool to retrieve the schema of a specific form. Use after confirming the form ID. |
| `FEATHERY_FORM_LIST` | List Forms | Tool to list all forms in your Feathery account. Use when you need to view or filter available forms. |
| `FEATHERY_FORM_LIST_HIDDEN_FIELDS` | List Hidden Fields | Tool to list all hidden form fields in the account. Use when you need to retrieve hidden field configuration before processing form submissions. |
| `FEATHERY_LOG_LIST_API_CONNECTOR_ERRORS` | List API Connector Errors | Tool to list recent API connector error logs for a form. Use after confirming the form ID to troubleshoot integration issues. |
| `FEATHERY_LOG_LIST_EMAIL_ISSUES` | List Email Issues | Tool to list email bounce and complaint events. Use when you need to diagnose delivery issues for sent emails. |
| `FEATHERY_LOG_LIST_EMAILS` | List Email Logs | Tool to list recently sent emails for a form. Use when you need to review email logs after sending form-based emails. |
| `FEATHERY_LOG_LIST_QUICK_REQUESTS` | List Quik Request Logs | Tool to list recent Quik integration request logs for a form. Use when you need to review API requests sent to Quik for a specific form. |
| `FEATHERY_USER_CREATE_OR_FETCH` | Create or Fetch User | Tool to create a new user or fetch an existing one. Use when you need to ensure a user exists and get their SDK key. |
| `FEATHERY_USER_DELETE` | Delete User | Tool to delete a specific user by ID. Use when you need to remove a user after confirming the user exists. |
| `FEATHERY_USER_GET_ALL_DATA` | Get All User Data | Tool to retrieve all stored data fields for a user. Use when you need to fetch all field entries associated with a specific user. |
| `FEATHERY_USER_GET_SESSION` | Get User Session | Tool to get a user's form session and progress. Use after authenticating and when you need to retrieve session data. |
| `FEATHERY_USER_LIST` | List Users | Tool to list all users in your Feathery account. Use when you need to retrieve users with optional creation time or field-based filters. |
| `FEATHERY_WORKSPACE_GENERATE_LOGIN_TOKEN` | Generate Workspace Login Token | Tool to generate a login JWT for a workspace. Use after obtaining the workspace and account IDs to get a token. |

## Supported Triggers

None listed.

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

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

mcp_url = session.mcp.url
```

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

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

## Related Toolkits

- [Apilio](https://composio.dev/toolkits/apilio) - Apilio is a home automation platform that lets you connect and control smart devices from different brands. It helps you build flexible automations with complex conditions, schedules, and integrations.
- [Basin](https://composio.dev/toolkits/basin) - Basin is a no-code form backend for quickly setting up reliable contact forms. It lets you collect and manage form submissions without writing any server-side code.
- [Bouncer](https://composio.dev/toolkits/bouncer) - Bouncer is an email validation platform that verifies the authenticity of email addresses in real-time and batch. It helps boost deliverability and reduce bounce rates for your communications.
- [Conveyor](https://composio.dev/toolkits/conveyor) - Conveyor is a platform that automates security reviews with a Trust Center and AI-driven questionnaire automation. It streamlines compliance and vendor security processes for faster, hassle-free reviews.
- [Crowdin](https://composio.dev/toolkits/crowdin) - Crowdin is a localization management platform that streamlines translation workflows and collaboration. It helps teams centralize multilingual content, boost productivity, and automate translation processes.
- [Databox](https://composio.dev/toolkits/databox) - Databox is a business analytics platform that connects your data from any tool and device. It helps you track KPIs, build dashboards, and discover actionable insights.
- [Detrack](https://composio.dev/toolkits/detrack) - Detrack is a delivery management platform for real-time tracking and proof of delivery. It helps businesses automate notifications and keep customers updated every step of the way.
- [Dnsfilter](https://composio.dev/toolkits/dnsfilter) - Dnsfilter is a cloud-based DNS security and content filtering solution. It helps organizations block online threats and manage safe internet access with ease.
- [Faraday](https://composio.dev/toolkits/faraday) - Faraday lets you embed AI in workflows across your stack for smarter automation. It boosts your favorite tools with actionable intelligence and seamless integration.
- [Fillout forms](https://composio.dev/toolkits/fillout_forms) - Fillout forms is an online platform for building and managing forms with a flexible API. It lets you create, distribute, and collect responses from forms with ease.
- [Formdesk](https://composio.dev/toolkits/formdesk) - Formdesk is an online form builder for creating and managing professional forms. It's perfect for collecting data, automating workflows, and integrating form submissions with your favorite services.
- [Formsite](https://composio.dev/toolkits/formsite) - Formsite lets you build online forms and surveys with drag-and-drop simplicity. Capture, manage, and integrate form responses securely for streamlined workflows.
- [Graphhopper](https://composio.dev/toolkits/graphhopper) - GraphHopper is an enterprise-grade Directions API for routing, optimization, and geocoding across multiple vehicle types. It enables fast, reliable route planning and logistics automation for businesses.
- [Hyperbrowser](https://composio.dev/toolkits/hyperbrowser) - Hyperbrowser is a next-generation platform for scalable browser automation. It empowers AI agents to interact with web apps, automate workflows, and handle browser sessions at scale.
- [La Growth Machine](https://composio.dev/toolkits/lagrowthmachine) - La Growth Machine automates multi-channel sales outreach and routine tasks for sales teams. Streamline your workflow and focus on closing more deals.
- [Leverly](https://composio.dev/toolkits/leverly) - Leverly is a workflow automation platform that connects and coordinates actions across your apps. It streamlines repetitive processes so your business runs smoother, faster, and with fewer manual steps.
- [Maintainx](https://composio.dev/toolkits/maintainx) - Maintainx is a cloud-based CMMS for centralizing maintenance data, communication, and workflows. It helps organizations streamline maintenance operations and improve team coordination.
- [Make](https://composio.dev/toolkits/make) - Make is an automation platform that connects your favorite apps and services. Build powerful, custom workflows without writing code.
- [Ntfy](https://composio.dev/toolkits/ntfy) - Ntfy is a notification service to send push messages to phones or desktops. Instantly deliver alerts and updates to users, devices, or teams.
- [Persona](https://composio.dev/toolkits/persona) - Persona offers identity infrastructure to automate user verification and compliance. It helps organizations securely verify users and reduce fraud risk.

## Frequently Asked Questions

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

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

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

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

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