# How to integrate TinyPNG MCP with LangChain

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

## Introduction

This guide walks you through connecting TinyPNG to LangChain 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 LangChain 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)
- [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
- Connect your TinyPNG project to Composio
- Create a Tool Router MCP session for TinyPNG
- Initialize an MCP client and retrieve TinyPNG tools
- Build a LangChain agent that can interact with TinyPNG
- Set up an interactive chat interface for testing

## What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.
Key features include:
- Agent Framework: Build agents that can use tools and make decisions
- MCP Integration: Connect to external services through Model Context Protocol adapters
- Memory Management: Maintain conversation history across interactions
- Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

## 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 agent to TinyPNG. It provides structured and secure access so your agent can perform TinyPNG 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

No description provided.

### 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

No description provided.
```python
pip install composio-langchain langchain-mcp-adapters langchain python-dotenv
```

```typescript
npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv
```

### 3. Set up environment variables

Create a .env file in your project root.
What's happening:
- COMPOSIO_API_KEY authenticates your requests to Composio's API
- COMPOSIO_USER_ID identifies the user for session management
- OPENAI_API_KEY enables access to OpenAI's language models
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here
```

### 4. Import dependencies

No description provided.
```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
```

```typescript
import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
```

### 5. Initialize Composio client

What's happening:
- We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
- Creating a Composio instance that will manage our connection to TinyPNG tools
- Validating that COMPOSIO_USER_ID is also set before proceeding
```python
async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
```

```typescript
const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
```

### 6. Create a Tool Router session

What's happening:
- We're creating a Tool Router session that gives your agent access to TinyPNG tools
- The create method takes the user ID and specifies which toolkits should be available
- The returned session.mcp.url is the MCP server URL that your agent will use
- This approach allows the agent to dynamically load and use TinyPNG tools as needed
```python
# Create Tool Router session for TinyPNG
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['tinypng']
)

url = session.mcp.url
```

```typescript
const session = await composio.create(
    userId as string,
    {
        toolkits: ['tinypng']
    }
);

const url = session.mcp.url;
```

### 7. Configure the agent with the MCP URL

No description provided.
```python
client = MultiServerMCPClient({
    "tinypng-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
```

```typescript
const client = new MultiServerMCPClient({
    "tinypng-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
```

### 8. Set up interactive chat interface

No description provided.
```python
conversation_history = []

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

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

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

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
```

```typescript
let conversationHistory: any[] = [];

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

const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: '
});

rl.prompt();

rl.on('line', async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
        console.log("\nGoodbye!");
        rl.close();
        process.exit(0);
    }

    if (!trimmedInput) {
        rl.prompt();
        return;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
```

### 9. Run the application

No description provided.
```python
if __name__ == "__main__":
    asyncio.run(main())
```

```typescript
main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
```

## Complete Code

```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['tinypng']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "tinypng-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any TinyPNG related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

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

```typescript
import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['tinypng']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "tinypng-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any TinyPNG related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    rl.prompt();

    rl.on('line', async (userInput: string) => {
        const trimmedInput = userInput.trim();
        
        if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
            console.log("\nGoodbye!");
            rl.close();
            process.exit(0);
        }
        
        if (!trimmedInput) {
            rl.prompt();
            return;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\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

You've successfully built a LangChain agent that can interact with TinyPNG through Composio's Tool Router.
Key features of this implementation:
- Dynamic tool loading through Composio's Tool Router
- Conversation history maintenance for context-aware responses
- Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

## 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)
- [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

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- [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.
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- [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.
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- [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 LangChain?

Yes, you can. LangChain 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.

---
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