How to Use the Xero MCP in LlamaIndex

Q: If I use LlamaIndex, can I find old payment records in Xero?

Yep. By indexing listxeropayments results, you build a history. You can query past sessions to check payments applied to invoices years ago.

Build knowledge bases from Xero financial data with LlamaIndex and the MCP Server.

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Connect Xero MCP to LlamaIndex

Create your Vinkius account to connect Xero to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Xero with LlamaIndex

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Indexing Xero Financial Data

The `get_balance_sheet` tool returns assets, liabilities, and equity totals. Instead of just printing this out, LlamaIndex indexes that data into your vector store. Later on, you can query past balance sheets—even from previous months' sessions—and get answers grounded in the actual API data.

Retrieving Detailed Invoice Info

You use `get_invoice_details` to pull specific Xero invoice records. LlamaIndex turns those details into searchable chunks of knowledge. This means you can ask, 'What was the total amount on that invoice from Q3?' and get a direct answer without running another API call.

Mapping Account Structures

Want to know what accounts exist? Call `list_chart_of_accounts` first. LlamaIndex then indexes this entire list, making it part of your searchable knowledge base. This lets you combine account types with transaction data from `list_bank_transactions`, answering complex questions that require context.

Setup guide

Set up Xero 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 Xero 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 Xero tools.",
)
response = await agent.run("List recent Xero data")

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Common questions about Xero MCP in LlamaIndex

LlamaIndex indexes the results. If you run a report using the MCP Server, those raw P&L numbers get stored in your vector database. You can then ask natural language questions about them later.

Yes. It treats all connected servers equally when indexing data. As long as the tool output is structured, it gets added to your unified knowledge base.

The MCP Server touches invoice details and bank transactions. The developer controls how that indexed data is stored within your secure vector store environment.

Yep. By indexing `list_xero_payments` results, you build a history. You can query past sessions to check payments applied to invoices years ago.

You get access to chart of accounts, contact lists, financial reports (P&L), and bank transaction records. All this structured data becomes queryable knowledge.

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