How to Use the Wise MCP in LlamaIndex

Q: How does Wise and LlamaIndex handle transfer history?

Run listtransfers using the MCP Server. This data gets indexed, meaning you can query past transactions semantically later on, even if you don't remember exact dates or IDs.

Q: Can I combine documents with live data from Wise and LlamaIndex?

Yes. By indexing the output of tools like getbalance, your application combines static text (documents) with dynamic API facts, giving you a single source of truth for answering questions.

Q: Does Wise and LlamaIndex support quote creation?

Yes. The createquote tool output can be indexed. This lets you search not just for past quotes, but for the parameters (currencies, rates) that generated them.

Q: Is listing all transfers reliable with Wise and LlamaIndex?

The listtransfers tool provides a comprehensive list of transactions. Indexing this data makes it highly queryable, giving you reliability for historical financial analysis.

Index financial knowledge with LlamaIndex using Wise MCP Server.

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Connect Wise MCP to LlamaIndex

Create your Vinkius account to connect Wise 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 Wise with LlamaIndex

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Building a Financial Knowledge Base for LlamaIndex

LlamaIndex turns API results into searchable knowledge. When your agent runs `list_statements`, the returned account statements aren't just displayed—they get indexed into your vector store. You can then query past financial activity later, retrieving answers grounded in real data. The MCP Server output becomes part of a unified index alongside your documents. This means you can ask things like, 'What was my balance on X date?' and the agent draws from both document context and live API data.

Retrieving Multi-Currency Balances via LlamaIndex

Need to know what funds are available across different accounts? Run `list_balances` through your LlamaIndex application. The resulting multi-currency list is indexed, allowing semantic search against it. Instead of just seeing a raw list, you can ask complex questions about the balances. This capability helps developers build robust RAG applications for finance. You combine historical context with live data points like `get_exchange_rate` to create deep insights.

Tracking Transfers and Profiles using MCP Server

An agent can use the `list_profiles` tool, and that resulting list of business profiles is indexed. Later queries can then reference specific profile IDs for follow-up actions, like checking details with `get_balance`. This provides a structured way to manage complex organizational data. Furthermore, indexing results from `list_recipients` ensures you never have to re-enter payee information; your knowledge base already holds the necessary records.

Setup guide

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

Run `list_transfers` using the MCP Server. This data gets indexed, meaning you can query past transactions semantically later on, even if you don't remember exact dates or IDs.

Yes. By indexing the output of tools like `get_balance`, your application combines static text (documents) with dynamic API facts, giving you a single source of truth for answering questions.

The server touches account statement data. When indexing this, ensure your vector store and access controls are highly restricted to maintain compliance with financial data standards.

Yes. The `create_quote` tool output can be indexed. This lets you search not just for past quotes, but for the parameters (currencies, rates) that generated them.

The `list_transfers` tool provides a comprehensive list of transactions. Indexing this data makes it highly queryable, giving you reliability for historical financial analysis.

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