How to Use the Smarty MCP in LlamaIndex

Q: Can LlamaIndex use Smarty for US address lookups?

Yes. Running validateusaddress and indexing the returned DPV status gives LlamaIndex a reliable source of truth, reducing hallucinations about physical locations.

Q: Is international addressing supported by LlamaIndex with Smarty?

Yes. You can use autocompleteintladdress to generate context and then store the result, meaning future queries can reference global address standards accurately.

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Connect Smarty MCP to LlamaIndex

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

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Setup Smarty with LlamaIndex

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Indexing Validation Results

The output of `validate_us_address` becomes indexed knowledge. If a user asks, 'What was the validation status of 123 Main St last week?', LlamaIndex searches this stored API data, not just its memory.

Parsing Documents for Search

Don't just pass documents to your LLM. Use `extract_addresses` first. It pulls out every physical address from raw text—emails or PDFs—and indexes those specific addresses into your vector store. This makes the data highly searchable.

Contextual Autocomplete Retrieval

You can index successful predictions. When a user is typing, LlamaIndex retrieves context from historical `autocomplete_intl_address` results. The model gets grounded suggestions based on past usage patterns stored in the vector base.

Setup guide

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

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Common questions about Smarty MCP in LlamaIndex

Smarty turns volatile API calls into stable, searchable knowledge. You index the structured outputs from tools like `validate_intl_address`, allowing your LLM to reference real-time location data.

Yes. Running `validate_us_address` and indexing the returned DPV status gives LlamaIndex a reliable source of truth, reducing hallucinations about physical locations.

It provides highly structured location intelligence: verified address components, coordinates, and validation statuses. This is perfect for creating an authoritative knowledge graph within your index.

Yes. You can use `autocomplete_intl_address` to generate context and then store the result, meaning future queries can reference global address standards accurately.

The server touches physical address data (US and International). The results of these validation tools are indexed into your knowledge base, maintaining strict control over the data's usage.

Use it with your favorite AI tools

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

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