# How to integrate Taggun MCP with CrewAI

```json
{
  "title": "How to integrate Taggun MCP with CrewAI",
  "toolkit": "Taggun",
  "toolkit_slug": "taggun",
  "framework": "CrewAI",
  "framework_slug": "crew-ai",
  "url": "https://composio.dev/toolkits/taggun/framework/crew-ai",
  "markdown_url": "https://composio.dev/toolkits/taggun/framework/crew-ai.md",
  "updated_at": "2026-05-12T10:27:48.117Z"
}
```

## Introduction

This guide walks you through connecting Taggun to CrewAI using the Composio tool router. By the end, you'll have a working Taggun agent that can extract vendor and total from this receipt image url, list all line items from uploaded invoice link, validate this receipt url before submitting expense through natural language commands.
This guide will help you understand how to give your CrewAI 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

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

## TL;DR

Here's what you'll learn:
- Get a Composio API key and configure your Taggun connection
- Set up CrewAI with an MCP enabled agent
- Create a Tool Router session or standalone MCP server for Taggun
- Build a conversational loop where your agent can execute Taggun operations

## What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.
Key features include:
- Agent Roles: Define specialized agents with specific goals and backstories
- Task Management: Create tasks with clear descriptions and expected outputs
- Crew Orchestration: Combine agents and tasks into collaborative workflows
- MCP Integration: Connect to external tools through Model Context Protocol

## 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.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `TAGGUN_ADD_MERCHANT_NAME` | Add Merchant Name | Tool to add a merchant name keyword to your account's model for predicting merchant names. Use when you want to improve merchant name recognition by training the model with specific merchant names. Changes to your account's model are updated daily and will affect future receipt processing. |
| `TAGGUN_EXPORT_KNOWN_MERCHANTS` | Export Known Merchants | Export the complete list of known merchants used for merchant name normalization in Taggun. Returns CSV data with merchant details including location IDs, names, addresses, and coordinates. Use this when you need to retrieve the full merchant registry for synchronization, auditing, or analysis. No parameters required - this is a read-only GET operation. |
| `TAGGUN_EXPORT_KNOWN_PRODUCT_CODES` | Export Known Product Codes | Export the complete list of known product codes used for product normalization and matching in Taggun. Returns CSV data with product code information. Use this when you need to retrieve the full product code registry for synchronization, auditing, or analysis. No parameters required - this is a read-only GET operation. |
| `TAGGUN_EXPORT_PRODUCT_CATEGORIES` | Export Product Categories | Export a list of product categories and descriptions used for product categorization in CSV format. Returns CSV data with product category information for analysis or synchronization purposes. Use this when you need to retrieve the complete product category registry. |
| `TAGGUN_GENERATE_MERCHANTS_CSV` | Generate Merchants CSV | Generate a CSV file with mock merchant data for testing purposes. Creates a temporary CSV file with the specified number of merchant rows, including fields like name, alias, address, coordinates, contact info, and tags. Use this when you need sample merchant data for bulk import operations or testing merchant-related API endpoints. The generated CSV follows a standard format with 10 columns: name, alias, address, postcode, lat, lng, country, phone, email, tags. |
| `TAGGUN_IMPORT_KNOWN_MERCHANTS` | Import Known Merchants | Import a list of merchant names and addresses to normalize and match in CSV or TSV format. Use this when you need to bulk upload merchant data for name normalization and matching. File must be less than 20MB and contain merchant information in CSV or TSV format. |
| `TAGGUN_IMPORT_KNOWN_PRODUCT_CODES` | Import Known Product Codes | Tool to import a list of product codes in CSV or TSV format for normalization and matching. Use when you need to upload product code data to Taggun for receipt/invoice processing. The file should contain product codes with descriptions (e.g., code,description columns). |
| `TAGGUN_IMPORT_PRODUCT_CATEGORIES` | Import Product Categories | Import a list of product categories and descriptions for product categorization. Accepts CSV or TSV files (less than 20MB) with category and description columns. Use this when you need to bulk import product category data for matching during receipt processing. |
| `TAGGUN_TRANSCRIBE_RECEIPT_ENCODED_SIMPLE` | Transcribe Receipt from Base64 Encoded Image | Extract structured data from a receipt or invoice using base64 encoded image data. Provide a base64 encoded image (JPEG, PNG, PDF, GIF) along with filename and content type to get back extracted fields like total amount, date, merchant name, tax, line items, and confidence scores. Use this when you have receipt/invoice image data already encoded as base64 and need to digitize the data. The API uses machine learning OCR to detect and extract key fields automatically. |
| `TAGGUN_TRANSCRIBE_RECEIPT_ENCODED_VERBOSE` | Transcribe Receipt Encoded Verbose | Tool to transcribe a receipt using base64 encoded image in JSON payload and return detailed results. Use when you have a base64 encoded receipt image and require comprehensive output including line items, merchant details, and confidence levels. The image must be larger than 1x1 pixels to avoid validation errors. |
| `TAGGUN_TRANSCRIBE_RECEIPT_FILE_SIMPLE` | 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. Use when you need to digitize receipt data from a file (PDF, JPG, PNG, GIF, HEIC up to 20MB). The API uses OCR to detect and extract key fields. |
| `TAGGUN_URL` | Process Receipt via URL | Extract structured data from a receipt or invoice image using OCR. Provide a public URL to a receipt/invoice image (JPEG, PNG, PDF, GIF) and get back extracted fields like total amount, date, merchant name, tax, line items, and confidence scores. Use this when you need to digitize receipt/invoice data from a publicly accessible image URL. The API uses machine learning OCR to detect and extract key fields automatically. |
| `TAGGUN_URL_VALIDATION` | URL Validation | Tool to extract and validate receipt data from a URL. Processes a receipt image from a public URL and returns extracted fields with confidence levels to assess receipt authenticity. Use when you have a receipt URL and need to verify it contains valid receipt data. |
| `TAGGUN_URL_VERBOSE` | URL Verbose | Tool to process a receipt or invoice from a URL for detailed data extraction. Use when you have a publicly accessible receipt or invoice URL and require comprehensive output including line items, merchant details, and confidence metrics. Call after verifying the URL is reachable. |

## Supported Triggers

None listed.

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

The Taggun MCP server is an implementation of the Model Context Protocol that connects your AI agent to Taggun. It provides structured and secure access so your agent can perform Taggun 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 and API key
- A Taggun connection authorized in Composio
- An OpenAI API key for the CrewAI LLM
- Basic familiarity with Python

### 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

**What's happening:**
- composio connects your agent to Taggun via MCP
- crewai provides Agent, Task, Crew, and LLM primitives
- crewai-tools[mcp] includes MCP helpers
- python-dotenv loads environment variables from .env
```bash
pip install composio crewai crewai-tools[mcp] python-dotenv
```

### 3. Set up environment variables

Create a .env file in your project root.
What's happening:
- COMPOSIO_API_KEY authenticates with Composio
- USER_ID scopes the session to your account
- OPENAI_API_KEY lets CrewAI use your chosen OpenAI model
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here
```

### 4. Import dependencies

**What's happening:**
- CrewAI classes define agents and tasks, and run the workflow
- MCPServerHTTP connects the agent to an MCP endpoint
- Composio will give you a short lived Taggun MCP URL
```python
import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

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

### 5. Create a Composio Tool Router session for Taggun

**What's happening:**
- You create a Taggun only session through Composio
- Composio returns an MCP HTTP URL that exposes Taggun tools
```python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["taggun"])

url = session.mcp.url
```

### 6. Initialize the MCP Server

**What's Happening:**
- Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
- MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
- Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
- Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
- Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.
```python
server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )
```

### 7. Create a CLI Chatloop and define the Crew

**What's Happening:**
- Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
- Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
- Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
- Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
- Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
- Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.
```python
print("Chat started! Type 'exit' or 'quit' to end.\n")

conversation_context = ""

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

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

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[agent], tasks=[task], verbose=False)
    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
```

## Complete Code

```python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["taggun"],
)
url = session.mcp.url

# Configure LLM
llm = LLM(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY"),
)

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\n")

    conversation_context = ""

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

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

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")
```

## Conclusion

You now have a CrewAI agent connected to Taggun through Composio's Tool Router. The agent can perform Taggun operations through natural language commands.
Next steps:
- Add role-specific instructions to customize agent behavior
- Plug in more toolkits for multi-app workflows
- Chain tasks for complex multi-step operations

## How to build Taggun MCP Agent with another framework

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

## Related Toolkits

- [Excel](https://composio.dev/toolkits/excel) - Microsoft Excel is a robust spreadsheet application for organizing, analyzing, and visualizing data. It's the go-to tool for calculations, reporting, and flexible data management.
- [21risk](https://composio.dev/toolkits/_21risk) - 21RISK is a web app built for easy checklist, audit, and compliance management. It streamlines risk processes so teams can focus on what matters.
- [Abstract](https://composio.dev/toolkits/abstract) - Abstract provides a suite of APIs for automating data validation and enrichment tasks. It helps developers streamline workflows and ensure data quality with minimal effort.
- [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.
- [Agenty](https://composio.dev/toolkits/agenty) - Agenty is a web scraping and automation platform for extracting data and automating browser tasks—no coding needed. It streamlines data collection, monitoring, and repetitive online actions.
- [Ambee](https://composio.dev/toolkits/ambee) - Ambee is an environmental data platform providing real-time, hyperlocal APIs for air quality, weather, and pollen. Get precise environmental insights to power smarter decisions in your apps and workflows.
- [Ambient weather](https://composio.dev/toolkits/ambient_weather) - Ambient Weather is a platform for personal weather stations with a robust API for accessing local, real-time, and historical weather data. Get detailed environmental insights directly from your own sensors for smarter apps and automations.
- [Anonyflow](https://composio.dev/toolkits/anonyflow) - Anonyflow is a service for encryption-based data anonymization and secure data sharing. It helps organizations meet GDPR, CCPA, and HIPAA data privacy compliance requirements.
- [Api ninjas](https://composio.dev/toolkits/api_ninjas) - Api ninjas offers 120+ public APIs spanning categories like weather, finance, sports, and more. Developers use it to supercharge apps with real-time data and actionable endpoints.
- [Api sports](https://composio.dev/toolkits/api_sports) - Api sports is a comprehensive sports data platform covering 2,000+ competitions with live scores and 15+ years of stats. Instantly access up-to-date sports information for analysis, apps, or chatbots.
- [Apify](https://composio.dev/toolkits/apify) - Apify is a cloud platform for building, deploying, and managing web scraping and automation tools called Actors. It lets you automate data extraction and workflow tasks at scale—no infrastructure headaches.
- [Autom](https://composio.dev/toolkits/autom) - Autom is a lightning-fast search engine results data platform for Google, Bing, and Brave. Developers use it to access fresh, low-latency SERP data on demand.
- [Beaconchain](https://composio.dev/toolkits/beaconchain) - Beaconchain is a real-time analytics platform for Ethereum 2.0's Beacon Chain. It provides detailed insights into validators, blocks, and overall network performance.
- [Big data cloud](https://composio.dev/toolkits/big_data_cloud) - BigDataCloud provides APIs for geolocation, reverse geocoding, and address validation. Instantly access reliable location intelligence to enhance your applications and workflows.
- [Bigpicture io](https://composio.dev/toolkits/bigpicture_io) - BigPicture.io offers APIs for accessing detailed company and profile data. Instantly enrich your applications with up-to-date insights on 20M+ businesses.
- [Bitquery](https://composio.dev/toolkits/bitquery) - Bitquery is a blockchain data platform offering indexed, real-time, and historical data from 40+ blockchains via GraphQL APIs. Get unified, reliable access to complex on-chain data for analytics, trading, and research.
- [Brightdata](https://composio.dev/toolkits/brightdata) - Brightdata is a leading web data platform offering advanced scraping, SERP APIs, and anti-bot tools. It lets you collect public web data at scale, bypassing blocks and friction.
- [Builtwith](https://composio.dev/toolkits/builtwith) - BuiltWith is a web technology profiler that uncovers the technologies powering any website. Gain actionable insights into analytics, hosting, and content management stacks for smarter research and lead generation.
- [Byteforms](https://composio.dev/toolkits/byteforms) - Byteforms is an all-in-one platform for creating forms, managing submissions, and integrating data. It streamlines workflows by centralizing form data collection and automation.
- [Cabinpanda](https://composio.dev/toolkits/cabinpanda) - Cabinpanda is a data collection platform for building and managing online forms. It helps streamline how you gather, organize, and analyze responses.

## Frequently Asked Questions

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

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.

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

Yes, you can. CrewAI 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.

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

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.

### 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 Taggun data and credentials are handled as safely as possible.

---
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