How to Use the Airwallex MCP in LlamaIndex

Q: How does LlamaIndex index data from my Airwallex account?

The framework calls tools like listpayments and converts the JSON outputs into document nodes. LlamaIndex then indexes these nodes into your vector store for semantic search.

Q: Can I search Airwallex beneficiary details using LlamaIndex?

Yes, you can. By exposing listbeneficiaries to your agent, LlamaIndex searches and matches recipient details using natural language queries.

Index your Airwallex transaction history into LlamaIndex to query balances and transfers with semantic search.

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Connect Airwallex MCP to LlamaIndex

Create your Vinkius account to connect Airwallex 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 Airwallex with LlamaIndex

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Index transaction logs with this LlamaIndex MCP Server

The `list_payments` tool fetches your recent transaction history so LlamaIndex can index it for semantic retrieval. This turns flat transaction logs into a searchable knowledge base where your agent answers complex financial queries. Instead of writing custom database connectors, you feed live payment data directly into your vector store via MCP. Your queries are grounded in real, up-to-date transaction records.

Semantic search over beneficiary records

The `list_beneficiaries` tool retrieves your global recipient list to build a searchable directory within LlamaIndex. Your agent searches this directory using natural language to find matching vendors or partners. This eliminates manual lookup scripts. The agent matches messy user queries to the exact beneficiary records stored in your account.

Context-aware treasury queries

The `get_balance` tool pulls your current multi-currency holdings through the MCP Server to provide context for LlamaIndex financial reports. The agent combines this balance data with `list_transfers` to build a complete picture of your cash flow. By indexing these live values, your RAG pipeline avoids using stale financial data. Your agent answers questions about your cash position with exact, real-time numbers.

Setup guide

Set up Airwallex 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 Airwallex 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 Airwallex tools.",
)
response = await agent.run("List recent Airwallex 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 Airwallex. 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 Airwallex MCP in LlamaIndex

The framework calls tools like `list_payments` and converts the JSON outputs into document nodes. LlamaIndex then indexes these nodes into your vector store for semantic search.

Yes, you can. By exposing `list_beneficiaries` to your agent, LlamaIndex searches and matches recipient details using natural language queries.

You control the caching behavior in your index configuration. For real-time tools like `get_fx_rates`, you should bypass the index cache to ensure your agent always retrieves live currency rates.

You install the MCP tool spec package and initialize it with your server URL. From there, you convert the tools to a standard tool list and pass them to your agent.

Your payment records and beneficiary details are encrypted in transit and never stored on the Vinkius platform. The sandbox environment is completely ephemeral, ensuring your private financial records remain secure.

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