How to Use the Cora Bank MCP in LlamaIndex

Index live Cora Bank financial records directly into your LlamaIndex vector store for grounded RAG.

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Connect Cora Bank MCP to LlamaIndex

Create your Vinkius account to connect Cora Bank 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 Cora Bank with LlamaIndex

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Query your Cora Bank transaction history using LlamaIndex

The `cora_get_statement` tool pulls your raw accounting history and cashflow data into your LlamaIndex pipeline. Instead of reading raw JSON, your application indexes these records so you can run semantic searches over past transactions. This setup prevents your model from hallucinating numbers. By feeding `cora_list_payments` straight into your index, your query engine answers financial questions using verified bank data.

Build a RAG engine with this MCP Server

This MCP Server allows your LlamaIndex agent to pull live billing statuses using `cora_get_invoice`. You can combine this live API data with offline PDFs or spreadsheets in a unified knowledge index. The agent uses `cora_list_invoices` to gather bulk invoice data, converting the raw text into searchable nodes. This lets you ask natural language questions about outstanding boletos and get grounded responses.

Ground financial decisions on real-time balances

The `cora_get_balance` tool retrieves your current cash reserves so your LlamaIndex agent can evaluate financial health. Before recommending any budget allocations, the agent inspects this balance to verify available funds. If a payment needs to go out, the agent runs `cora_send_pix` using the exact bank routing details retrieved from your indexed supplier database. This guarantees every transfer matches your actual ledger.

Setup guide

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

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Common questions about Cora Bank MCP in LlamaIndex

Yes. By loading your billing records into your pipeline, you can ingest invoice details, vectorize them, and run semantic queries to find unpaid boletos.

Install the LlamaIndex MCP tool spec and initialize the client. Use the tool spec to load the tools, then pass them to your function agent to start querying.

LlamaIndex does not cache these results by default. Calls to the balance tool hit the live API to ensure your financial reports are accurate down to the second.

Use the allowed tools filter when initializing your MCP tool specification. You can expose only read tools and block write actions like sending transfers.

Yes. Your sensitive statement data and balance sheets are processed in memory inside the V8 sandbox. Vinkius handles the credentials, so LlamaIndex never sees your raw API keys.

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