How to Use the Brankas MCP in LlamaIndex

Q: Can I index Brankas transaction histories inside LlamaIndex?

Yes. You can fetch raw transaction logs using gettransaction and convert them into Document objects. This lets your LlamaIndex pipeline index the payment records for quick semantic search.

Q: How do I filter Brankas tools in my LlamaIndex agent?

You can use the allowedtools filter when setting up the MCP tool spec. This allows you to restrict the agent so it only accesses getbalance while blocking access to payout tools.

Q: Can I use this MCP server for real-time treasury RAG?

Yes. You can query live bank data by combining getbalance with your vector index. The agent pulls live numbers to answer questions, ensuring your treasury reports are accurate.

Q: How does this server protect my private bank balance data?

Balance data fetched via getbalance is routed through a zero-trust V8 sandbox. The server never logs or caches your account balances, keeping your financial statements isolated from third-party storage.

Index live bank transactions and account balances into LlamaIndex to query your financial data without hallucinations.

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Connect Brankas MCP to LlamaIndex

Create your Vinkius account to connect Brankas 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 Brankas with LlamaIndex

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Turn bank statements into queryable knowledge

Grab transaction logs using `get_statement` to feed live financial histories directly into your index using this MCP tool. This lets you ask natural language questions about your spending patterns instead of reading raw tables. Because LlamaIndex updates the vector store dynamically, your queries are always grounded in actual financial data. The agent matches your questions against the indexed statements to give you accurate, hallucination-free answers.

Ground financial decisions in real-time balances

Run `get_balance` to fetch current account values and prevent your RAG pipelines from acting on stale data. This ensures any financial advice generated by your agent is backed by real-time numbers. You can combine these live balances with historical documents in your index. The agent synthesizes a complete financial picture, letting you run complex audits without leaving your LlamaIndex workflow.

Securely index Brankas MCP Server identity data

Call `get_identities` to fetch official bank KYC records and build a searchable registry of verified customers. You can quickly look up customer names or business profiles to verify details. This MCP integration connects live API responses to your document store. Instead of manually cross-referencing files, your agent searches the local index to confirm if an identity matches the incoming checkout request from `create_checkout`.

Setup guide

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

Yes. You can fetch raw transaction logs using `get_transaction` and convert them into Document objects. This lets your LlamaIndex pipeline index the payment records for quick semantic search.

You can use the allowed_tools filter when setting up the MCP tool spec. This allows you to restrict the agent so it only accesses `get_balance` while blocking access to payout tools.

The client coordinates API requests through Vinkius, which handles rate limiting on the backend. Your LlamaIndex agent can run multiple loops of `get_statement` without manually managing API headers or backoffs.

Yes. You can query live bank data by combining `get_balance` with your vector index. The agent pulls live numbers to answer questions, ensuring your treasury reports are accurate.

Balance data fetched via `get_balance` is routed through a zero-trust V8 sandbox. The server never logs or caches your account balances, keeping your financial statements isolated from third-party storage.

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