How to Use the NachoNacho MCP in LlamaIndex

Q: How do I load NachoNacho MCP tools into LlamaIndex?

Install llama-index-tools-mcp. Set up a BasicMCPClient, wrap it in an McpToolSpec, and call totoollistasync() to feed the functions to your agent.

Q: How do I restrict which functions the agent can use?

Pass an allowedtools filter when configuring the tool spec. You grant access to read-only functions while blocking card creation.

Index your payment history via this MCP Server into LlamaIndex for semantic search.

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Connect NachoNacho MCP to LlamaIndex

Create your Vinkius account to connect NachoNacho 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 NachoNacho with LlamaIndex

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Claude

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Semantic Search for Financial Data

`list_transactions` extracts your complete SaaS payment history so LlamaIndex embeds it into a vector store. You stop writing SQL queries to find out why your hosting bill spiked. Instead, you ask your RAG application questions in plain English, and it retrieves the exact transaction records. The index updates continuously. Every time a new charge hits a virtual card, the tool pulls the data, chunks it, and adds it to your knowledge base. You query past spending patterns grounded entirely in actual API data, not LLM guesses.

Grounded SaaS Subscription Queries

`list_subscriptions` and `get_subscription` feed vendor details directly into your LlamaIndex documents. Your application knows exactly which department owns which software tool. When a user asks about active licenses, the agent pulls the exact subscription metadata from the index. Mixing this with internal policy documents creates a unified procurement oracle. The agent cross-references active subscriptions against approved vendor lists, instantly flagging unapproved shadow IT spending.

NachoNacho MCP Server Action Agents

`create_card` and `freeze_card` turn your static RAG application into an active participant. A user queries the index for unused subscriptions. The agent finds three dormant accounts and immediately offers to suspend the associated payment methods. You approve the action, and the agent executes the freeze commands. The system then re-indexes the updated card statuses using `list_cards`, ensuring your knowledge base remains perfectly synced with your live financial state.

Setup guide

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

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Common questions about NachoNacho MCP in LlamaIndex

Install `llama-index-tools-mcp`. Set up a `BasicMCPClient`, wrap it in an `McpToolSpec`, and call `to_tool_list_async()` to feed the functions to your agent.

Yes. You pull the data using the list tools and embed the JSON responses into your vector store for semantic querying.

Pass an `allowed_tools` filter when configuring the tool spec. You grant access to read-only functions while blocking card creation.

No. Because the agent pulls live data via the API and indexes the exact numerical limits, its answers are grounded in reality.

This MCP setup exposes vendor names, charge amounts, and timestamps. Vinkius secures the pipeline with a zero-trust architecture, meaning no persistent connection remains open once the vector ingestion completes.

Use it with your favorite AI tools

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