How to Use the IBAN.com MCP in LlamaIndex

Q: How do I load the IBAN.com MCP Server tools into my LlamaIndex pipeline?

Run pip install llama-index-tools-mcp and instantiate the BasicMCPClient with the MCP server URL. Wrap it in McpToolSpec and call totoollistasync() to pass the tools to your FunctionAgent.

Q: Can LlamaIndex cache the results of IBAN.com validation queries?

Yes, LlamaIndex indexes the output of getbankdetails into your vector database. Future queries for the same bank information can be resolved locally from the index, saving API credits.

Q: How does the IBAN.com MCP Server handle SWIFT code verification in LlamaIndex?

Your agent calls validateswiftbic to check the format and existence of the BIC. The verified routing data is then formatted as a document and stored in your index for future semantic search.

Q: Can I monitor my API usage balance from within a LlamaIndex query?

Yes, your RAG agent can call getapibalance to retrieve your current credit count. It can then inject this number directly into the system prompt to warn you when credits are low.

Index verified bank metadata directly into your LlamaIndex vector store for hallucination-free financial search.

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Connect IBAN.com MCP to LlamaIndex

Create your Vinkius account to connect IBAN.com 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 IBAN.com with LlamaIndex

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Index verified bank metadata into LlamaIndex

The `get_bank_details` tool extracts official bank names, addresses, and branch codes directly from an IBAN. LlamaIndex then indexes this raw metadata into your vector store, turning ephemeral API responses into queryable knowledge. This setup eliminates database lookups for recurring bank queries. Your RAG pipeline searches the indexed output of past validation runs to answer user questions with verified facts instead of LLM guesses.

Validate and query credit card formats via RAG

The `validate_card_luhn` tool validates card numbers using the standard mathematical formula before your agent commits them to a transaction record. LlamaIndex logs the outcome of these checks, building an audit trail of valid and invalid card patterns. Your search agent can query this historical log to identify patterns in failed checkouts. Instead of hitting external endpoints repeatedly, the agent checks the local vector index first.

Ground financial queries with real-time MCP Server data

The `validate_iban` tool verifies account structures in real-time to prevent transaction failures. When your LlamaIndex agent receives an account query, it runs this validation step to ensure the data is accurate before indexing it. This live verification step ensures your vector index remains a source of truth. Your agent relies on this MCP Server tool to keep your financial knowledge base clean and accurate.

Setup guide

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

Run `pip install llama-index-tools-mcp` and instantiate the `BasicMCPClient` with the MCP server URL. Wrap it in `McpToolSpec` and call `to_tool_list_async()` to pass the tools to your `FunctionAgent`.

Yes, LlamaIndex indexes the output of `get_bank_details` into your vector database. Future queries for the same bank information can be resolved locally from the index, saving API credits.

Your agent calls `validate_swift_bic` to check the format and existence of the BIC. The verified routing data is then formatted as a document and stored in your index for future semantic search.

Yes, your RAG agent can call `get_api_balance` to retrieve your current credit count. It can then inject this number directly into the system prompt to warn you when credits are low.

No, the server processes IBANs and SWIFT codes in a secure, ephemeral V8 sandbox. All data transmissions are encrypted, and no financial records are persisted on Vinkius infrastructure.

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