How to Use the Finmo MCP in LlamaIndex

Q: Can I index Finmo customer records into a vector database with LlamaIndex?

Yes, you can use listcustomers to pull customer data and index it using LlamaIndex vector store integrations with this MCP. This allows your agent to perform semantic searches over your client base.

Q: How often should LlamaIndex query Finmo getfxrate to keep rates updated?

You should query getfxrate dynamically during the agent's execution loop rather than indexing it. FX rates change constantly, so calling the tool directly ensures your agent uses the most current conversion rate.

Q: Does this MCP Server support LlamaIndex tool filtering?

Yes, you can use the allowedtools filter in your LlamaIndex setup to restrict access. For example, you can allow the agent to run listpayins and listrefunds while hiding the write tools.

Index real-time Finmo ledger data and wallet balances directly into your LlamaIndex RAG pipelines.

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Connect Finmo MCP to LlamaIndex

Create your Vinkius account to connect Finmo 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 Finmo with LlamaIndex

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Search Finmo payout histories with LlamaIndex MCP Server

The `list_payouts` tool retrieves historical payout records so your LlamaIndex pipeline can index them for vector search. Your agent queries this index to answer complex questions about past transactions without writing SQL queries. By converting the raw API payloads into searchable nodes, you can ask your agent about specific payment anomalies. The system pulls the relevant documents, matches them with `list_refunds`, and gives you a grounded answer — and that is it — no manual tracking required.

Ground agent responses in live wallet balances

The `list_wallets` tool fetches real-time balances across your global accounts to feed directly into LlamaIndex query engines. This prevents your agent from hallucinating financial figures when generating treasury reports. The agent combines this live balance data with vector-stored historical data to forecast cash flow. It checks current holdings, looks up incoming funds via `list_payins`, and outputs accurate financial summaries.

Index local payout rails and beneficiary data

The `list_beneficiaries` tool lists your registered payees and their banking details for indexing. LlamaIndex uses this data to map out payout destinations and ensure your agent knows where funds are going. When you need to verify a routing method, the agent checks the index against `list_virtual_accounts` to find the most cost-effective path. This removes the guesswork from cross-border payment routing.

Setup guide

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

LlamaIndex uses the Finmo MCP Server to pull live records via `list_wallets` and `list_payouts` before generating answers. Because the LLM reads actual API payloads instead of guessing, the responses are grounded in real financial facts.

Yes, you can use `list_customers` to pull customer data and index it using LlamaIndex vector store integrations with this MCP. This allows your agent to perform semantic searches over your client base.

You should query `get_fx_rate` dynamically during the agent's execution loop rather than indexing it. FX rates change constantly, so calling the tool directly ensures your agent uses the most current conversion rate.

Yes, you can use the `allowed_tools` filter in your LlamaIndex setup to restrict access. For example, you can allow the agent to run `list_payins` and `list_refunds` while hiding the write tools.

Your customer names and banking details are processed through a zero-trust V8 sandbox on Vinkius. The MCP Server executes ephemerally, ensuring that sensitive financial profiles are never cached on our servers.

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