How to Use the Nubarium MCP in LlamaIndex

Q: How do I ingest Nubarium tool outputs into a LlamaIndex vector store?

You convert the outputs of getmexcompanydetails or validatemexrfc into LlamaIndex Document objects. From there, your pipeline embeds and stores them in your vector database for semantic retrieval.

Q: Can LlamaIndex query the Nubarium MCP Server during a retrieval step?

Yes, you can register the MCP Server as an McpToolSpec within your LlamaIndex agent. The agent will decide to call the Mexican registry tools when its vector search lacks up-to-date corporate data.

Q: How does LlamaIndex prevent hallucinations when verifying Mexican RFC tax IDs?

The agent calls validatemexrfc to fetch real-time registration details from the government database. LlamaIndex then forces the LLM to synthesize its answer using only this retrieved context.

Q: What is the fastest way to set up the Nubarium MCP Server with LlamaIndex?

Install llama-index-tools-mcp and point the client to your Vinkius endpoint. This exposes all six validation tools to your LlamaIndex agent in just a few lines of code.

Index Mexican corporate registries and identity verification data directly into your LlamaIndex RAG pipelines.

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Connect Nubarium MCP to LlamaIndex

Create your Vinkius account to connect Nubarium 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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Indexing Corporate Registries with LlamaIndex

`get_mex_company_details` retrieves the complete corporate profile of a Mexican business and feeds it directly into your LlamaIndex document store via the MCP Server. Your RAG pipeline indexes this structured data, making complex corporate structures and registration dates searchable via natural language. This prevents your agent from hallucinating corporate details when answering questions about business entities. You query the index, and LlamaIndex retrieves the exact, verified facts pulled from the Mexican registry.

Semantic Search Over Verified Entities

`search_mex_companies` allows your LlamaIndex query engine to look up Mexican businesses and index the search results on the fly. The engine stores these search outputs in a local vector database, creating a cache of verified corporate names and registration IDs. When a user asks about a business, LlamaIndex first checks this cached vector index before hitting the live API. This setup drastically reduces your API consumption and speeds up response times for common queries.

Grounding Identity Verification in Live Data

`validate_mex_curp` extracts official identity details that your LlamaIndex agent uses to ground user profiles. The agent injects the verified name and status directly into the context window, ensuring that downstream synthesis relies on official records. You can also track your API limits by calling `get_api_usage` and indexing the consumption metrics. LlamaIndex uses this indexed usage data to alert you when your verification budget is running low.

Setup guide

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

You convert the outputs of `get_mex_company_details` or `validate_mex_rfc` into LlamaIndex Document objects. From there, your pipeline embeds and stores them in your vector database for semantic retrieval.

Yes, you can register the MCP Server as an `McpToolSpec` within your LlamaIndex agent. The agent will decide to call the Mexican registry tools when its vector search lacks up-to-date corporate data.

The agent calls `validate_mex_rfc` to fetch real-time registration details from the government database. LlamaIndex then forces the LLM to synthesize its answer using only this retrieved context.

Install `llama-index-tools-mcp` and point the client to your Vinkius endpoint. This exposes all six validation tools to your LlamaIndex agent in just a few lines of code.

All RFC, CURP, and corporate registry data retrieved from the Mexican government pass through a zero-trust V8 sandbox on Vinkius. Your data is processed entirely in memory, meaning no identity records are stored or logged on the host platform.

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