How to Use the Konfío MCP in LlamaIndex

Q: How do I connect the Konfío MCP Server to LlamaIndex?

It's straightforward. After installing llama-index-tools-mcp, create a BasicMCPClient with your Vinkius URL. Then, wrap it in McpToolSpec and call totoollistasync() to get the tools for your agent.

Q: Can LlamaIndex query past Konfío invoices I've created?

Yes, that's the core idea. When your agent uses listinvoices, LlamaIndex can index the results. Later, you can ask natural language questions about those invoices, and the agent will retrieve the answers from its knowledge base.

Q: How does LlamaIndex use Konfío to manage credit cards?

You can build a RAG agent that periodically calls listcctransactions and indexes the spending data. Then you can ask it questions like "did we pay for our hosting bill this month?" and it will find the answer in the indexed transaction history.

Ground your LlamaIndex RAG apps in live Konfío financial data.

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Connect Konfío MCP to LlamaIndex

Create your Vinkius account to connect Konfío 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 Konfío with LlamaIndex

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Index Your Financial History

LlamaIndex doesn't just call the Konfío tools; it remembers the answers. Use `list_cc_transactions` and `list_invoices` to pull your financial history, and LlamaIndex will automatically index this data into a vector store. Now your agent's knowledge is based on your actual numbers. This creates a searchable history of your business finances. You can ask questions like "show me all invoices for client X from last quarter" or "what were our biggest credit card expenses in May?". The agent will query the indexed data from past tool calls to give you a precise answer.

Build a Financial Query Engine

Combine live tool calls with indexed knowledge. When you ask about a loan, your agent can use `get_loan_details` for the current status while also querying its index for historical payment data from previous `make_loan_payment` calls. You get the full picture, instantly. This turns your agent into a true financial analyst. It can correlate data across different sources—linking an expense from `list_cc_transactions` to a specific received invoice from `list_invoices`—because it has access to both live data from the MCP server and an indexed history.

Augment Decisions with Real Data using LlamaIndex

Before making a decision, your agent can gather context. For example, before calling `create_transfer` to pay a supplier, it can query its knowledge base for past payment amounts and frequencies to that same supplier. This prevents errors and provides better context for your actions. This MCP Server provides the raw data your RAG application needs. By using `McpToolSpec` and passing the tools to a `FunctionAgent`, you give LlamaIndex the ability to both act on your behalf and learn from the results of those actions.

Setup guide

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

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Common questions about Konfío MCP in LlamaIndex

It's straightforward. After installing `llama-index-tools-mcp`, create a `BasicMCPClient` with your Vinkius URL. Then, wrap it in `McpToolSpec` and call `to_tool_list_async()` to get the tools for your agent.

Yes, that's the core idea. When your agent uses `list_invoices`, LlamaIndex can index the results. Later, you can ask natural language questions about those invoices, and the agent will retrieve the answers from its knowledge base.

You can build a RAG agent that periodically calls `list_cc_transactions` and indexes the spending data. Then you can ask it questions like "did we pay for our hosting bill this month?" and it will find the answer in the indexed transaction history.

LlamaIndex adds a memory layer. Instead of just getting a one-time response from `get_business_profile`, the agent indexes that profile. This lets it answer future questions without making another API call and allows it to reason about how data has changed over time.

Your data is only accessed for specific tool calls you authorize. The Konfío MCP server processes data like your business profile or transaction lists within a secure, isolated environment for each request. The connection is encrypted, and your Vinkius token is the only key.

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