How to Use the Square MCP in LlamaIndex

Q: When using LlamaIndex, how do I query past Square payment records?

You run listpayments and include those results in your index. Later, you can use a natural language query against the index to retrieve specific details, like finding all payments over $50 last month.

Q: Does LlamaIndex help with multi-location reconciliation for Square?

Yes. You run liststorelocations and then index the results of multiple searchorders calls, grouping them by location ID. This creates a unified knowledge graph covering all your branches.

Q: What happens if I try to find customer details using LlamaIndex?

You run listcustomers, and the output is indexed. When you ask about a specific person, the system doesn't just call getcustomer; it searches the index for all related records first.

Q: How do I combine inventory data with LlamaIndex?

You can run getstockcount and index those levels. This lets you ask, 'What is the current stock count for item X at location Y?' and get an answer based on your indexed API results.

Index Square API Data into Vector Stores with LlamaIndex for Knowledge-Augmented Retrieval.

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Connect Square MCP to LlamaIndex

Create your Vinkius account to connect Square to LlamaIndex — 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 LlamaIndex

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Indexing Payment History

The `list_payments` tool fetches historical transaction data. By indexing this output, your RAG application turns raw payment records into searchable knowledge chunks. You can query past sessions and get answers grounded in actual API results.

Storing Customer Profiles

You use `list_customers` to pull user data. Passing this through LlamaIndex means that customer details become part of a persistent, searchable index. This lets you answer questions like 'What was John Doe's last order?' without making repeated API calls.

Retrieving Order Metadata

`get_order_details` retrieves specific sales information. When this data is indexed, it allows users to ask complex questions about an order—like 'What was the discount on last Tuesday's sale?'—and get a direct answer from the stored knowledge base.

Setup guide

Set up Square MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Square MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Square tools.",
)
response = await agent.run("List recent Square data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Square. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Square MCP in LlamaIndex

You run `list_payments` and include those results in your index. Later, you can use a natural language query against the index to retrieve specific details, like finding all payments over $50 last month.

Yes. You run `list_store_locations` and then index the results of multiple `search_orders` calls, grouping them by location ID. This creates a unified knowledge graph covering all your branches.

You run `list_customers`, and the output is indexed. When you ask about a specific person, the system doesn't just call `get_customer`; it searches the index for all related records first.

You can run `get_stock_count` and index those levels. This lets you ask, 'What is the current stock count for item X at location Y?' and get an answer based on your indexed API results.

The server touches customer contact information and payment transaction metadata. When indexing, ensure you redact PII before storing it in the vector store to maintain compliance.

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