# How to integrate L2s MCP with Pydantic AI

```json
{
  "title": "How to integrate L2s MCP with Pydantic AI",
  "toolkit": "L2s",
  "toolkit_slug": "l2s",
  "framework": "Pydantic AI",
  "framework_slug": "pydantic-ai",
  "url": "https://composio.dev/toolkits/l2s/framework/pydantic-ai",
  "markdown_url": "https://composio.dev/toolkits/l2s/framework/pydantic-ai.md",
  "updated_at": "2026-05-12T10:17:05.469Z"
}
```

## Introduction

This guide walks you through connecting L2s to Pydantic AI using the Composio tool router. By the end, you'll have a working L2s agent that can shorten this long url for sharing, list all shortened links i created, get analytics for a specific short link through natural language commands.
This guide will help you understand how to give your Pydantic AI agent real control over a L2s account through Composio's L2s MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate L2s with

- [OpenAI Agents SDK](https://composio.dev/toolkits/l2s/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/l2s/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/l2s/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/l2s/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/l2s/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/l2s/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/l2s/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/l2s/framework/cli)
- [Google ADK](https://composio.dev/toolkits/l2s/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/l2s/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/l2s/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/l2s/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/l2s/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/l2s/framework/crew-ai)

## TL;DR

Here's what you'll learn:
- How to set up your Composio API key and User ID
- How to create a Composio Tool Router session for L2s
- How to attach an MCP Server to a Pydantic AI agent
- How to stream responses and maintain chat history
- How to build a simple REPL-style chat interface to test your L2s workflows

## What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.
Key features include:
- Type Safety: Built on Pydantic for automatic data validation
- MCP Support: Native support for Model Context Protocol servers
- Streaming: Built-in support for streaming responses
- Async First: Designed for async/await patterns

## What is the L2s MCP server, and what's possible with it?

The L2s MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your L2s account. It provides structured and secure access to your shortened URLs, analytics, and account settings, so your agent can create short links, fetch analytics, generate QR codes, and manage your team collaboration workflows on your behalf.
- Instant URL shortening: Let your agent take any long link and generate a concise, professional short URL in seconds.
- Detailed analytics retrieval: Ask the agent to fetch in-depth stats and metadata about any of your shortened URLs, including performance and engagement metrics.
- Comprehensive short link management: Have your agent list all your previously created short URLs, making it easy to find, share, or analyze them as needed.
- User account setting insights: Fetch and review your L2s account preferences—from email and notification settings to appearance options—directly through your agent.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `L2S_GET_URL_DETAILS` | Get URL Details | Tool to retrieve details of a shortened URL. Use when you need metadata about a specific shortened link. Use after obtaining a valid URL ID. |
| `L2S_L2_S_GET_USER_SETTINGS` | Get User Settings | Tool to retrieve current user's settings. Use after authentication to fetch email, notification, and appearance preferences. |
| `L2S_L2_S_LIST_URLS` | List Shortened URLs | Tool to list all shortened URLs. Use after user authentication to retrieve the user's URLs with pagination details. |
| `L2S_L2_S_SHORTEN_URL` | Shorten URL with L2S | Tool to shorten a given long URL. Use when you need a concise link representation. |

## Supported Triggers

None listed.

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

The L2s MCP server is an implementation of the Model Context Protocol that connects your AI agent to L2s. It provides structured and secure access so your agent can perform L2s 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:
- Python 3.9 or higher
- A Composio account with an active API key
- Basic familiarity with Python and async programming

### 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 the required libraries.
What's happening:
- composio connects your agent to external SaaS tools like L2s
- pydantic-ai lets you create structured AI agents with tool support
- python-dotenv loads your environment variables securely from a .env file
```bash
pip install composio pydantic-ai python-dotenv
```

### 3. Set up environment variables

Create a .env file in your project root.
What's happening:
- COMPOSIO_API_KEY authenticates your agent to Composio's API
- USER_ID associates your session with your account for secure tool access
- OPENAI_API_KEY to access OpenAI LLMs
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key
```

### 4. Import dependencies

What's happening:
- We load environment variables and import required modules
- Composio manages connections to L2s
- MCPServerStreamableHTTP connects to the L2s MCP server endpoint
- Agent from Pydantic AI lets you define and run the AI assistant
```python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
```

### 5. Create a Tool Router Session

What's happening:
- We're creating a Tool Router session that gives your agent access to L2s 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
```python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for L2s
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["l2s"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
```

### 6. Initialize the Pydantic AI Agent

What's happening:
- The MCP client connects to the L2s endpoint
- The agent uses GPT-5 to interpret user commands and perform L2s operations
- The instructions field defines the agent's role and behavior
```python
# Attach the MCP server to a Pydantic AI Agent
l2s_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[l2s_mcp],
    instructions=(
        "You are a L2s assistant. Use L2s tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
```

### 7. Build the chat interface

What's happening:
- The agent reads input from the terminal and streams its response
- L2s API calls happen automatically under the hood
- The model keeps conversation history to maintain context across turns
```python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with L2s.\n")

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", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
```

### 8. Run the application

What's happening:
- The asyncio loop launches the agent and keeps it running until you exit
```python
if __name__ == "__main__":
    asyncio.run(main())
```

## Complete Code

```python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for L2s
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["l2s"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    l2s_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[l2s_mcp],
        instructions=(
            "You are a L2s assistant. Use L2s tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with L2s.\n")

    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", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

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

## Conclusion

You've built a Pydantic AI agent that can interact with L2s through Composio's Tool Router. With this setup, your agent can perform real L2s actions through natural language.
You can extend this further by:
- Adding other toolkits like Gmail, HubSpot, or Salesforce
- Building a web-based chat interface around this agent
- Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + L2s for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.

## How to build L2s MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/l2s/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/l2s/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/l2s/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/l2s/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/l2s/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/l2s/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/l2s/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/l2s/framework/cli)
- [Google ADK](https://composio.dev/toolkits/l2s/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/l2s/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/l2s/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/l2s/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/l2s/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/l2s/framework/crew-ai)

## Related Toolkits

- [Reddit](https://composio.dev/toolkits/reddit) - Reddit is a social news platform with thriving user-driven communities (subreddits). It's the go-to place for discussion, content sharing, and viral marketing.
- [Facebook](https://composio.dev/toolkits/facebook) - Facebook is a social media and advertising platform for businesses and creators. It helps you connect, share, and manage content across your public Facebook Pages.
- [Linkedin](https://composio.dev/toolkits/linkedin) - LinkedIn is a professional networking platform for connecting, sharing content, and engaging with business opportunities. It's the go-to place for building your professional brand and unlocking new career connections.
- [Active campaign](https://composio.dev/toolkits/active_campaign) - ActiveCampaign is a marketing automation and CRM platform for managing email campaigns, sales pipelines, and customer segmentation. It helps businesses engage customers and drive growth through smart automation and targeted outreach.
- [ActiveTrail](https://composio.dev/toolkits/active_trail) - ActiveTrail is a user-friendly email marketing and automation platform. It helps you reach subscribers and automate campaigns with ease.
- [Ahrefs](https://composio.dev/toolkits/ahrefs) - Ahrefs is an SEO and marketing platform for site audits, keyword research, and competitor insights. It helps you improve search rankings and drive organic traffic.
- [Amcards](https://composio.dev/toolkits/amcards) - AMCards lets you create and mail personalized greeting cards online. Build stronger customer relationships with easy, automated card campaigns.
- [Beamer](https://composio.dev/toolkits/beamer) - Beamer is a news and changelog platform for in-app announcements and feature updates. It helps companies boost user engagement by sharing news where users are most active.
- [Benchmark email](https://composio.dev/toolkits/benchmark_email) - Benchmark Email is a platform for creating, sending, and tracking email campaigns. It's built to help you engage audiences and analyze results—all in one place.
- [Bigmailer](https://composio.dev/toolkits/bigmailer) - BigMailer is an email marketing platform for managing multiple brands with white-labeling and automation. It helps teams streamline campaigns and simplify integration with Amazon SES.
- [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.
- [Brevo](https://composio.dev/toolkits/brevo) - Brevo is an all-in-one email and SMS marketing platform for transactional messaging, automation, and CRM. It helps businesses engage customers and streamline communications through powerful campaign tools.
- [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 L2s MCP?

With a standalone L2s MCP server, the agents and LLMs can only access a fixed set of L2s tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from L2s and many other apps based on the task at hand, all through a single MCP endpoint.

### Can I use Tool Router MCP with Pydantic AI?

Yes, you can. Pydantic AI 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 L2s tools.

### Can I manage the permissions and scopes for L2s while using Tool Router?

Yes, absolutely. You can configure which L2s 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 L2s data and credentials are handled as safely as possible.

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