How to Use the Square MCP in Google ADK

Q: What is the best way to list customer details with Square and Google ADK?

The agent uses listcustomers first, then allows you to filter or deepen your query using searchcustomers. This structured approach ensures that every detail retrieved about a user's account is available in a single, massive context window.

Q: Can I retrieve inventory levels using Square and Google ADK?

Yes. You first identify the location with liststorelocations, then use that ID to query getstockcount. This allows you to track resource consumption alongside financial data in BigQuery.

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Connect Square MCP to Google ADK

Create your Vinkius account to connect Square to Google ADK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Square with Google ADK

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Analyze massive payment records

Because you're working with BigQuery, you can ingest high volumes of raw Square data. You use `list_payments` to pull paginated transaction lists, which then feed into your long-context Gemini models for analysis. The agent doesn't just read the data; it interprets it. It processes payment metadata from `get_payment_details`, allowing you to spot complex patterns across millions of records that standard reporting misses.

Build comprehensive customer profiles

You can build complete customer views by linking multiple data points. The agent first retrieves a specific user via `get_customer` and then correlates that record with their order history using `search_orders`. This deep analysis is critical for targeted marketing. Since the ADK integrates with Vertex AI, you can use these combined datasets to train models that predict future sales behavior based on past Square interactions.

Synchronize inventory across sites

When managing multiple physical locations, consistency is key. You call `list_store_locations` first to define the scope of your operation. Then, you use `get_stock_count` for each specific location ID. The ADK lets you run these checks in a single, massive workflow, comparing inventory levels across all sites against historical sales data gathered from the MCP Server.

Setup guide

Set up Square MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Square tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Square_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Square tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Square MCP in Google ADK

You feed raw transaction logs into BigQuery using the ADK. The agent then runs complex SQL queries against this data, joining records from `get_payment_details` with order metadata to verify financial accuracy across all channels.

The agent uses `list_customers` first, then allows you to filter or deepen your query using `search_customers`. This structured approach ensures that every detail retrieved about a user's account is available in a single, massive context window.

The ADK supports long-context reasoning (1M+ tokens). This means you can pass huge sets of raw transaction records—like thousands of payments from `list_payments`—to Gemini for analysis without losing context.

Yes. You first identify the location with `list_store_locations`, then use that ID to query `get_stock_count`. This allows you to track resource consumption alongside financial data in BigQuery.

The server touches Customer Data (contact info, purchase history). Since the ADK is designed for enterprise scale, it's optimized to handle PII from millions of records while maintaining audit compliance.

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