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How to integrate Taggun MCP with Pydantic AI

Connect Pydantic AI to Taggun MCP. Extract vendor and total from this receipt image url, list all line items from uploaded invoice link, and more using natural language, with authentication handled for you.

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Taggun is a real-time receipt OCR API for extracting structured data from receipt images. Instantly turn receipts into actionable data for expense management and automation.

14 Tools

Introduction

This guide walks you through connecting Taggun to Pydantic AI using the Composio tool router. By the end, you'll have a working Taggun agent that can scan receipts from images or URLs, extract line items and totals, import and export known merchants, and manage product categories through natural language commands.

This guide will help you understand how to give your Pydantic AI agent real control over a Taggun account through Composio's Taggun MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Taggun with

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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 Taggun
  • 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 Taggun 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 Taggun MCP server, and what's possible with it?

The Taggun MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Taggun account. It provides structured and secure access to real-time receipt OCR and merchant management, so your agent can scan receipts, extract detailed data, validate image URLs, and manage merchant records on your behalf.

  • Instant receipt data extraction: Have your agent process receipt or invoice images via public URLs to pull out structured purchase data quickly and accurately.
  • Detailed line item analysis: Use verbose extraction to get comprehensive data including line items, merchant info, and confidence metrics from receipt images or PDFs.
  • Automated merchant registry management: Export the full list of known merchants for audits or synchronize merchant data directly through your agent.
  • Receipt image URL validation: Let your agent check if a receipt image URL meets campaign and validation requirements before processing.
  • Generate merchant mock CSVs for testing: Easily create sample merchant CSV files to test or bulk import merchant data as part of your automation workflow.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step09 STEPS
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
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard 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.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Taggun
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

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
5

Import dependencies

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()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Taggun
  • MCPServerStreamableHTTP connects to the Taggun MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

Create a Tool Router Session

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 Taggun
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["taggun"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Taggun 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
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
taggun_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[taggun_mcp],
    instructions=(
        "You are a Taggun assistant. Use Taggun tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Taggun endpoint
  • The agent uses GPT-5 to interpret user commands and perform Taggun operations
  • The instructions field defines the agent's role and behavior
8

Build the chat interface

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 Taggun.\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()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Taggun API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

Run the application

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

Complete Code

Here's the complete code to get you started with Taggun and Pydantic AI:

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 Taggun
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["taggun"],
    )
    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
    taggun_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[taggun_mcp],
        instructions=(
            "You are a Taggun assistant. Use Taggun 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 Taggun.\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 Taggun through Composio's Tool Router. With this setup, your agent can perform real Taggun 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 + Taggun for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
TOOLS

Supported Tools

Every Taggun action and event your agent gets out of the box.

Add Merchant Name

Tool to add a merchant name keyword to your account's model for predicting merchant names.

Export Known Merchants

Export the complete list of known merchants used for merchant name normalization in Taggun.

Export Known Product Codes

Export the complete list of known product codes used for product normalization and matching in Taggun.

Export Product Categories

Export a list of product categories and descriptions used for product categorization in CSV format.

Generate Merchants CSV

Generate a CSV file with mock merchant data for testing purposes.

Import Known Merchants

Import a list of merchant names and addresses to normalize and match in CSV or TSV format.

Import Known Product Codes

Tool to import a list of product codes in CSV or TSV format for normalization and matching.

Import Product Categories

Import a list of product categories and descriptions for product categorization.

Transcribe Receipt from Base64 Encoded Image

Extract structured data from a receipt or invoice using base64 encoded image data.

Transcribe Receipt Encoded Verbose

Transcribe a receipt from a base64 encoded image and return detailed results, including merchant details and confidence levels.

Transcribe Receipt File (Simple)

Tool to upload a receipt or invoice image file and extract basic data including merchant name, total amount, tax amount, and date.

Process Receipt via URL

Extract structured data from a receipt or invoice image using OCR.

URL Validation

Extract receipt data from a public URL and return extracted fields with confidence levels for the extraction.

URL Verbose

Tool to process a receipt or invoice from a URL for detailed data extraction.

FRAMEWORKS

How to build Taggun MCP Agent with another framework

Claude Code logo

Claude Code

Use Taggun MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Taggun MCP with Claude Cowork

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Taggun MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Taggun MCP with Claude Agent SDK

Codex logo

Codex

Use Taggun MCP with Codex

OpenClaw logo

OpenClaw

Use Taggun MCP with OpenClaw

Hermes logo

Hermes

Use Taggun MCP with Hermes

Atomic Agent logo

Atomic Agent

Use Taggun MCP with Atomic Agent

Google ADK logo

Google ADK

Use Taggun MCP with Google ADK

LangChain logo

LangChain

Use Taggun MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Taggun MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Taggun MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Taggun MCP with LlamaIndex

CrewAI logo

CrewAI

Use Taggun MCP with CrewAI

AutoGen logo

AutoGen

Use Taggun MCP with AutoGen

FAQ

Frequently asked questions

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

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 Taggun tools.

Yes, absolutely. You can configure which Taggun 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.

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 Taggun data and credentials are handled as safely as possible.

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