# How to integrate TinyPNG MCP with Autogen

```json
{
  "title": "How to integrate TinyPNG MCP with Autogen",
  "toolkit": "TinyPNG",
  "toolkit_slug": "tinypng",
  "framework": "AutoGen",
  "framework_slug": "autogen",
  "url": "https://composio.dev/toolkits/tinypng/framework/autogen",
  "markdown_url": "https://composio.dev/toolkits/tinypng/framework/autogen.md",
  "updated_at": "2026-05-12T10:28:40.911Z"
}
```

## Introduction

This guide walks you through connecting TinyPNG to AutoGen using the Composio tool router. By the end, you'll have a working TinyPNG agent that can compress uploaded png and return link, optimize this jpeg and report savings, convert this image to webp format through natural language commands.
This guide will help you understand how to give your AutoGen agent real control over a TinyPNG account through Composio's TinyPNG MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate TinyPNG with

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

## TL;DR

Here's what you'll learn:
- Get and set up your OpenAI and Composio API keys
- Install the required dependencies for Autogen and Composio
- Initialize Composio and create a Tool Router session for TinyPNG
- Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
- Configure an Autogen AssistantAgent that can call TinyPNG tools
- Run a live chat loop where you ask the agent to perform TinyPNG operations

## What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.
Key features include:
- Multi-Agent Systems: Build collaborative agent workflows
- MCP Workbench: Native support for Model Context Protocol tools
- Streaming HTTP: Connect to external services through streamable HTTP
- AssistantAgent: Pre-built agent class for tool-using assistants

## What is the TinyPNG MCP server, and what's possible with it?

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

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `TINYPNG_GET_COMPRESSION_COUNT` | Get TinyPNG Compression Count | Tool to retrieve the number of compressions made this month. Use when you need to monitor your TinyPNG API usage. |
| `TINYPNG_SHRINK_AND_GET_IMAGE_ID` | Shrink and get image ID | Tool to shrink an image and return its TinyPNG image ID. Use when you need only the compressed image identifier from the API response Location header. |
| `TINYPNG_TINIFY_COMPRESS_AND_STORE_IN_AZURE` | Compress and Store Image in Azure | Compress an image using the Tinify API and upload the optimized result directly to Azure Blob Storage in a single operation. The image is first compressed by Tinify, then uploaded to the specified Azure Blob URL using the provided SAS token. Use this when you need to optimize images and store them in Azure without intermediate steps. Supports JPEG, PNG, and WebP image formats. |
| `TINYPNG_TINIFY_OUTPUT` | Download Compressed Image | Tool to retrieve a compressed image by its image ID. Use after compressing an image to download the result. |
| `TINYPNG_TRANSFORM_IMAGE` | Transform Compressed Image | Tool to transform a compressed image by resizing, converting format, preserving metadata, or storing to cloud storage. Use when you have an image ID from a previous compression and need to apply transformations. |

## Supported Triggers

None listed.

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

The TinyPNG MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to TinyPNG. Instead of manually wiring TinyPNG APIs, OAuth, and scopes yourself, you get a structured, tool-based interface that an LLM can call safely.
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

You will need:
- A Composio API key
- An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
- A TinyPNG account you can connect to Composio
- Some basic familiarity with Autogen and Python async

### 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).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

Install Composio, Autogen extensions, and dotenv.
What's happening:
- composio connects your agent to TinyPNG via MCP
- autogen-agentchat provides the AssistantAgent class
- autogen-ext-openai provides the OpenAI model client
- autogen-ext-tools provides MCP workbench support
```bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools
```

### 3. Set up environment variables

Create a .env file in your project folder.
What's happening:
- COMPOSIO_API_KEY is required to talk to Composio
- OPENAI_API_KEY is used by Autogen's OpenAI client
- USER_ID is how Composio identifies which user's TinyPNG connections to use
```bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com
```

### 4. Import dependencies and create Tool Router session

What's happening:
- load_dotenv() reads your .env file
- Composio(api_key=...) initializes the SDK
- create(...) creates a Tool Router session that exposes TinyPNG tools
- session.mcp.url is the MCP endpoint that Autogen will connect to
```python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a TinyPNG session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["tinypng"]
    )
    url = session.mcp.url
```

### 5. Configure MCP parameters for Autogen

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.
What's happening:
- url points to the Tool Router MCP endpoint from Composio
- timeout is the HTTP timeout for requests
- sse_read_timeout controls how long to wait when streaming responses
- terminate_on_close=True cleans up the MCP server process when the workbench is closed
```python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)
```

### 6. Create the model client and agent

What's happening:
- OpenAIChatCompletionClient wraps the OpenAI model for Autogen
- McpWorkbench connects the agent to the MCP tools
- AssistantAgent is configured with the TinyPNG tools from the workbench
```python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create TinyPNG assistant agent with MCP tools
    agent = AssistantAgent(
        name="tinypng_assistant",
        description="An AI assistant that helps with TinyPNG operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )
```

### 7. Run the interactive chat loop

What's happening:
- The script prompts you in a loop with You:
- Autogen passes your input to the model, which decides which TinyPNG tools to call via MCP
- agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
- Typing exit, quit, or bye ends the loop
```python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any TinyPNG related question or task to the agent.\n")

# Conversation loop
while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
```

## Complete Code

```python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a TinyPNG session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["tinypng"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create TinyPNG assistant agent with MCP tools
        agent = AssistantAgent(
            name="tinypng_assistant",
            description="An AI assistant that helps with TinyPNG operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
        print("Ask any TinyPNG related question or task to the agent.\n")

        # Conversation loop
        while True:
            user_input = input("You: ").strip()

            if user_input.lower() in ['exit', 'quit', 'bye']:
                print("\nGoodbye!")
                break

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())
```

## Conclusion

You now have an Autogen assistant wired into TinyPNG through Composio's Tool Router and MCP. From here you can:
- Add more toolkits to the toolkits list, for example notion or hubspot
- Refine the agent description to point it at specific workflows
- Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for TinyPNG, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

## How to build TinyPNG MCP Agent with another framework

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

## Related Toolkits

- [Google Drive](https://composio.dev/toolkits/googledrive) - Google Drive is a cloud storage platform for uploading, sharing, and collaborating on files. It's perfect for keeping your documents accessible and organized across devices.
- [Google Sheets](https://composio.dev/toolkits/googlesheets) - Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly.
- [Notion](https://composio.dev/toolkits/notion) - Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place.
- [Airtable](https://composio.dev/toolkits/airtable) - Airtable combines the flexibility of spreadsheets with the power of a database for easy project and data management. Teams use Airtable to organize, track, and collaborate with custom views and automations.
- [Google Docs](https://composio.dev/toolkits/googledocs) - Google Docs is a cloud-based word processor that enables document creation and real-time collaboration. Its seamless sharing and version history make team editing and content management a breeze.
- [Google Super](https://composio.dev/toolkits/googlesuper) - Google Super is an all-in-one suite combining Gmail, Drive, Calendar, Sheets, Analytics, and more. It gives you a unified platform to manage your digital life, boosting productivity and organization.
- [Asana](https://composio.dev/toolkits/asana) - Asana is a collaborative work management platform for teams to organize and track projects. It streamlines teamwork, boosts productivity, and keeps everyone aligned on goals.
- [Google Tasks](https://composio.dev/toolkits/googletasks) - Google Tasks is a to-do list and task management tool integrated into Gmail and Google Calendar. It helps you organize, track, and complete tasks across your Google ecosystem.
- [Linear](https://composio.dev/toolkits/linear) - Linear is a modern issue tracking and project planning tool for fast-moving teams. It helps streamline workflows, organize projects, and boost productivity.
- [Jira](https://composio.dev/toolkits/jira) - Jira is Atlassian’s platform for bug tracking, issue tracking, and agile project management. It helps teams organize work, prioritize tasks, and deliver projects efficiently.
- [Clickup](https://composio.dev/toolkits/clickup) - ClickUp is an all-in-one productivity platform for managing tasks, docs, goals, and team collaboration. It streamlines project workflows so teams can work smarter and stay organized in one place.
- [Monday](https://composio.dev/toolkits/monday) - Monday.com is a customizable work management platform for project planning and collaboration. It helps teams organize tasks, automate workflows, and track progress in real time.
- [Figma](https://composio.dev/toolkits/figma) - Figma is a collaborative interface design tool for teams and individuals. It streamlines design workflows with real-time collaboration and easy sharing.
- [Abyssale](https://composio.dev/toolkits/abyssale) - Abyssale is a creative automation platform for generating images, videos, GIFs, PDFs, and HTML5 content programmatically. It streamlines and scales visual content production for marketing, design, and operations teams.
- [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time.
- [Affinda](https://composio.dev/toolkits/affinda) - Affinda is an AI-powered document processing platform that automates data extraction from resumes, invoices, and more. It streamlines document-heavy workflows by turning files into structured, actionable data.
- [Agiled](https://composio.dev/toolkits/agiled) - Agiled is an all-in-one business management platform for CRM, projects, and finance. It helps you streamline workflows, consolidate client data, and manage business processes in one place.
- [Agility cms](https://composio.dev/toolkits/agility_cms) - Agility CMS is a headless content management system for building and managing digital experiences across platforms. It lets teams update content quickly and deliver omnichannel experiences with ease.
- [Algodocs](https://composio.dev/toolkits/algodocs) - Algodocs is an AI-powered platform that automates data extraction from business documents. It delivers fast, secure, and accurate processing without templates or manual training.
- [Alttext ai](https://composio.dev/toolkits/alttext_ai) - AltText.ai is a service that generates alt text for images automatically. It helps boost accessibility and SEO for your visual content.

## Frequently Asked Questions

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

With a standalone TinyPNG MCP server, the agents and LLMs can only access a fixed set of TinyPNG tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from TinyPNG and many other apps based on the task at hand, all through a single MCP endpoint.

### Can I use Tool Router MCP with Autogen?

Yes, you can. Autogen 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 TinyPNG tools.

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

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

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