# How to integrate Taggun MCP with Claude Agent SDK

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

## Introduction

This guide walks you through connecting Taggun to the Claude Agent SDK 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 Claude Agent SDK 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 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)
- [CrewAI](https://composio.dev/toolkits/taggun/framework/crew-ai)

## TL;DR

Here's what you'll learn:
- Get and set up your Claude/Anthropic and Composio API keys
- Install the necessary dependencies
- Initialize Composio and create a Tool Router session for Taggun
- Configure an AI agent that can use Taggun as a tool
- Run a live chat session where you can ask the agent to perform Taggun operations

## What is Claude Agent SDK?

The Claude Agent SDK is Anthropic's official framework for building AI agents powered by Claude. It provides a streamlined interface for creating agents with MCP tool support and conversation management.
Key features include:
- Native MCP Support: Built-in support for Model Context Protocol servers
- Permission Modes: Control tool execution permissions
- Streaming Responses: Real-time response streaming for interactive applications
- Context Manager: Clean async context management for sessions

## 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:
- Composio API Key and Claude/Anthropic API Key
- Primary know-how of Claude Agents SDK
- A Taggun account
- Some knowledge of Python

### 1. Getting API Keys for Claude/Anthropic and Composio

Claude/Anthropic API Key
- Go to the [Anthropic Console](https://console.anthropic.com/settings/organization/api-keys) and create an API key. You'll need credits to use the models.
- 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-anthropic claude-agent-sdk python-dotenv
```

```typescript
npm install @anthropic-ai/claude-agent-sdk @composio/core 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 identifies the user for session management
- ANTHROPIC_API_KEY authenticates with Anthropic/Claude
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
ANTHROPIC_API_KEY=your_anthropic_api_key_here
```

### 4. Import dependencies

No description provided.
```python
import asyncio
from claude_agent_sdk import ClaudeSDKClient, ClaudeAgentOptions
import os
from composio import Composio
from dotenv import load_dotenv

load_dotenv()
```

```typescript
import 'dotenv/config';
import readline from 'node:readline';
import { Composio } from '@composio/core';
import { query, type Options } from "@anthropic-ai/claude-agent-sdk";

dotenv.config();
```

### 5. Create a Composio instance and Tool Router session

No description provided.
```python
async def chat_with_remote_mcp():
    api_key = os.getenv("COMPOSIO_API_KEY")
    if not api_key:
        raise RuntimeError("COMPOSIO_API_KEY is not set")

    composio = Composio(api_key=api_key)

    # Create Tool Router session for Taggun
    mcp_server = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["taggun"]
    )

    url = mcp_server.mcp.url

    if not url:
        raise ValueError("Session URL not found")
```

```typescript
async function chat() {
  const { COMPOSIO_API_KEY, USER_ID } = process.env;
  if (!COMPOSIO_API_KEY || !USER_ID) {
    throw new Error('COMPOSIO_API_KEY and USER_ID required in .env');
  }

  const composio = new Composio({ apiKey: COMPOSIO_API_KEY });

  // Create Tool Router session for Taggun
  const session = await composio.create(USER_ID, {
    toolkits: ['taggun'],
  });
  const mcpUrl = session?.mcp.url;
```

### 6. Configure Claude Agent with MCP

No description provided.
```python
# Configure remote MCP server for Claude
options = ClaudeAgentOptions(
    permission_mode="bypassPermissions",
    mcp_servers={
        "composio": {
            "type": "http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    },
    system_prompt="You are a helpful assistant with access to Taggun tools via Composio.",
    max_turns=10
)
```

```typescript
const options: Options = {
  permissionMode: 'bypassPermissions',
  mcpServers: {
    composio: {
      type: 'http',
      url: mcpUrl,
      headers: { 'x-api-key': COMPOSIO_API_KEY }
    }
  },
  systemPrompt: 'You are a helpful assistant with access to Taggun tools via Composio.',
  maxTurns: 10,
};
```

### 7. Create client and start chat loop

No description provided.
```python
# Create client with context manager
async with ClaudeSDKClient(options=options) as client:
    print("\nChat started. Type 'exit' or 'quit' to end.\n")

    # Main chat loop
    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit"}:
            print("Goodbye!")
            break

        # Send query
        await client.query(user_input)

        # Receive and print response
        print("Claude: ", end="", flush=True)
        async for message in client.receive_response():
            if hasattr(message, "content"):
                for block in message.content:
                    if hasattr(block, "text"):
                        print(block.text, end="", flush=True)
        print()
```

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

  console.log('\nChat started. Type "exit" to quit.\n');

  let isProcessing = false;

  async function ask(prompt: string) {
    isProcessing = true;
    rl.pause();

    process.stdout.write('Claude is thinking...');
    const stream = query({ prompt, options });

    let firstChunk = true;
    for await (const msg of stream) {
      const content = (msg as any).message?.content || (msg as any).content;
      if (Array.isArray(content)) {
        for (const block of content) {
          if (block.type === 'text' && block.text) {
            if (firstChunk) {
              process.stdout.write('\r\x1b[K');
              process.stdout.write('Claude: ');
              firstChunk = false;
            }
            process.stdout.write(block.text);
          }
        }
      }
    }
    process.stdout.write('\n\n');

    isProcessing = false;
    rl.resume();
    rl.prompt();
  }

  rl.on('line', async (line) => {
    if (isProcessing) return;

    const input = line.trim();
    if (input === 'exit') {
      rl.close();
      process.exit(0);
    }
    if (input) await ask(input);
    else rl.prompt();
  });

  await ask('What can you help me with?');
}
```

### 8. Run the application

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

```typescript
try {
  await chat();
} catch (error) {
  console.error(error);
  process.exit(1);
}
```

## Complete Code

```python
import asyncio
from claude_agent_sdk import ClaudeSDKClient, ClaudeAgentOptions
import os
from composio import Composio
from dotenv import load_dotenv

load_dotenv()

async def chat_with_remote_mcp():
    api_key = os.getenv("COMPOSIO_API_KEY")
    if not api_key:
        raise RuntimeError("COMPOSIO_API_KEY is not set")

    composio = Composio(api_key=api_key)

    # Create Tool Router session for Taggun
    mcp_server = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["taggun"]
    )

    url = mcp_server.mcp.url

    if not url:
        raise ValueError("Session URL not found")

    # Configure remote MCP server for Claude
    options = ClaudeAgentOptions(
        permission_mode="bypassPermissions",
        mcp_servers={
            "composio": {
                "type": "http",
                "url": url,
                "headers": {
                    "x-api-key": os.getenv("COMPOSIO_API_KEY")
                }
            }
        },
        system_prompt="You are a helpful assistant with access to Taggun tools via Composio.",
        max_turns=10
    )

    # Create client with context manager
    async with ClaudeSDKClient(options=options) as client:
        print("\nChat started. Type 'exit' or 'quit' to end.\n")

        # Main chat loop
        while True:
            user_input = input("You: ").strip()
            if user_input.lower() in {"exit", "quit"}:
                print("Goodbye!")
                break

            # Send query
            await client.query(user_input)

            # Receive and print response
            print("Claude: ", end="", flush=True)
            async for message in client.receive_response():
                if hasattr(message, "content"):
                    for block in message.content:
                        if hasattr(block, "text"):
                            print(block.text, end="", flush=True)
            print()

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

```typescript
import 'dotenv/config';
import readline from 'node:readline';
import { Composio } from '@composio/core';
import { query, type Options } from "@anthropic-ai/claude-agent-sdk";

async function chat() {
  const { COMPOSIO_API_KEY, USER_ID } = process.env;
  if (!COMPOSIO_API_KEY || !USER_ID) {
    throw new Error('COMPOSIO_API_KEY and USER_ID required in .env');
  }

  const composio = new Composio({ apiKey: COMPOSIO_API_KEY });
  const session = await composio.create(USER_ID, {
    toolkits: ['taggun']
  });
  const mcp_url = session?.mcp.url;

  const options: Options = {
    permissionMode: 'bypassPermissions',
    mcpServers: {
      composio: {
        type: 'http',
        url: mcp_url,
        headers: { 'x-api-key': COMPOSIO_API_KEY }
      }
    },
    systemPrompt: 'You are a helpful assistant with access to Taggun tools via Composio.',
    maxTurns: 10,
  };

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

  console.log('\nChat started. Type "exit" to quit.\n');

  let isProcessing = false;

  async function ask(prompt: string) {
    isProcessing = true;
    rl.pause();

    process.stdout.write('Claude is thinking...');
    const stream = query({ prompt, options });

    let firstChunk = true;
    for await (const msg of stream) {
      const content = (msg as any).message?.content || (msg as any).content;
      if (Array.isArray(content)) {
        for (const block of content) {
          if (block.type === 'text' && block.text) {
            if (firstChunk) {
              process.stdout.write('\r\x1b[K');
              process.stdout.write('Claude: ');
              firstChunk = false;
            }
            process.stdout.write(block.text);
          }
        }
      }
    }
    process.stdout.write('\n\n');

    isProcessing = false;
    rl.resume();
    rl.prompt();
  }

  rl.on('line', async (line) => {
    if (isProcessing) return;

    const input = line.trim();
    if (input === 'exit') {
      rl.close();
      process.exit(0);
    }
    if (input) await ask(input);
    else rl.prompt();
  });

  await ask('What can you help me with?');
}

try {
  await chat();
} catch (error) {
  console.error(error);
  process.exit(1);
}
```

## Conclusion

You've successfully built a Claude Agent SDK agent that can interact with Taggun through Composio's Tool Router.
Key features:
- Native MCP support through Claude's agent framework
- Streaming responses for real-time interaction
- Permission bypass for smooth automated workflows
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

## How to build Taggun MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/taggun/framework/open-ai-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)
- [CrewAI](https://composio.dev/toolkits/taggun/framework/crew-ai)

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- [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.
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## 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 Claude Agent SDK?

Yes, you can. Claude Agent SDK 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.

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