How to Use the Stark Bank MCP in LlamaIndex

Q: How does LlamaIndex use Stark Bank for knowledge augmentation?

It takes the output from tools like starkgetstatement and embeds that data. This means you can query your system about past finances using natural language, rather than needing to run a specific API call every time.

Q: Can LlamaIndex track payment progress with Stark Bank?

Yes. By indexing the outputs of starklisttransfers and starklistboletos, you create a historical record that answers questions like, 'Where are those payments currently stuck?'.

Q: Does LlamaIndex handle complex payments like transfers?

It indexes the results of starklisttransfers. This lets you build a query that pulls all finalized outgoing payment details and presents them as searchable knowledge.

Build RAG applications with LlamaIndex using Stark Bank data indexing.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers — Included with Plan

Connect Stark Bank MCP to LlamaIndex

Create your Vinkius account to connect Stark Bank to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

GDPR Included with Plan

Setup Stark Bank with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Indexing Financial Statements (LlamaIndex)

The output of `stark_get_statement` can be indexed directly into your vector store. This means you don't just get a raw PDF; you create a searchable knowledge base from the actual cash flow data. Later, you can query past periods or specific line items—like searching for 'all expenses related to vendor X in Q2'—and retrieve answers grounded in the API data.

Tracking Payment History (MCP Server)

LlamaIndex turns tool outputs into searchable context. When you call `stark_list_transfers` or `stark_list_boletos`, those results get indexed. You can then ask, 'What were the status updates on our payments last month?' and it pulls the exact data. This moves beyond simple lookup; it turns transactional history into usable knowledge.

Corporate Card Knowledge Retrieval (LlamaIndex)

You can index card setup procedures and usage rules. After running `stark_create_corporate_card`, the details of that card, along with its associated charges from `stark_list_pix_charges`, become part of your knowledge graph. Developers build RAG apps where live API data and internal documents are combined into one unified search index.

Setup guide

Set up Stark Bank 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 Stark Bank 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 Stark Bank tools.",
)
response = await agent.run("List recent Stark Bank data")

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Stark Bank. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Stark Bank now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Stark Bank MCP in LlamaIndex

It takes the output from tools like `stark_get_statement` and embeds that data. This means you can query your system about past finances using natural language, rather than needing to run a specific API call every time.

Yes. By indexing the outputs of `stark_list_transfers` and `stark_list_boletos`, you create a historical record that answers questions like, 'Where are those payments currently stuck?'.

The MCP Server provides access to almost every core banking function—from issuing cards (`stark_create_corporate_card`) to generating instant charges (`stark_create_pix_charge`). All these outputs are indexable.

It indexes the results of `stark_list_transfers`. This lets you build a query that pulls all finalized outgoing payment details and presents them as searchable knowledge.

The server provides access to transaction history, specifically listing charges from Pix and the detailed raw cash flow statements.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python