How to Use the CountryStateCity MCP in LlamaIndex

Q: Can I index CountryStateCity tool outputs into a LlamaIndex vector store?

Yes, you can run tools like liststates or getcountry and pass the returned geographic metadata directly to your index writer. This creates a semantic index of regional data that your RAG pipeline can search. It eliminates the need to manually download and parse massive geographic datasets.

Q: How does LlamaIndex handle the Supporter+ tier for CountryStateCity?

If your LlamaIndex agent attempts to call listcitiesbycountry without a Supporter+ tier, the MCP server returns a clear error message. Your agent can catch this exception and fallback to listcitiesbystate to gather the necessary city data.

Q: Can I filter which CountryStateCity tools are exposed to LlamaIndex?

Yes, you can use the allowedtools filter when initializing your LlamaIndex MCP tool spec. This lets you restrict your agent to only use getcountry and liststates, preventing it from making unnecessary city-level queries.

Index live global geographic data into your LlamaIndex vector store for hallucination-free location queries.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect CountryStateCity MCP to LlamaIndex

Create your Vinkius account to connect CountryStateCity 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.

GDPR Free for Subscribers

Setup CountryStateCity with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Build RAG Pipelines with Live Geographic Data

The CountryStateCity MCP Server feeds verified regional data directly into your LlamaIndex RAG pipelines. Use `get_country` and `get_state` to fetch real-time geographic records, then index those outputs directly into your vector database. LlamaIndex ensures your agent answers location queries using actual, verified data. This setup bridges the gap between static documents and live registries. Your RAG pipeline can query the vector store for historical context while pulling fresh ISO codes from this server.

Semantic Search Over Cached Location Metadata

This geographic MCP Server lets you index entire regional lists for fast semantic search. By using LlamaIndex to index the output of `list_states` and `list_countries`, you create a searchable local knowledge base of regional structures. Your agent searches this index first, only hitting the live tools when fresh data is required. This hybrid approach keeps your token usage down. Your agent gets the speed of a vector database combined with the accuracy of live geographic queries.

Feed LlamaIndex Agents Verified ISO Codes

This geographic metadata MCP Server provides structured ISO codes directly to your LlamaIndex agents. When your LlamaIndex agent needs to resolve a regional boundary, it can call `list_cities_by_state` to get an exact list of cities. The tool returns structured geographic metadata that fits perfectly into your agent's context window. You don't need to write custom clean-up scripts. The tool output is structured for immediate ingestion, allowing your agent to make decisions based on real-world geography.

Setup guide

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by CountryStateCity. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect CountryStateCity now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about CountryStateCity MCP in LlamaIndex

Yes, you can run tools like `list_states` or `get_country` and pass the returned geographic metadata directly to your index writer. This creates a semantic index of regional data that your RAG pipeline can search. It eliminates the need to manually download and parse massive geographic datasets.

If your LlamaIndex agent attempts to call `list_cities_by_country` without a Supporter+ tier, the MCP server returns a clear error message. Your agent can catch this exception and fallback to `list_cities_by_state` to gather the necessary city data.

Yes, you can use the `allowed_tools` filter when initializing your LlamaIndex MCP tool spec. This lets you restrict your agent to only use `get_country` and `list_states`, preventing it from making unnecessary city-level queries.

Instantiate the basic MCP client with your Vinkius endpoint, then wrap it in `McpToolSpec`. Call `to_tool_list_async()` and pass those tools straight to your LlamaIndex agent.

All geographic queries, ISO codes, and city lists are processed in an ephemeral sandbox. Your location queries are never cached on our servers or used to train models. Once LlamaIndex receives the geographic payload, the connection closes and all temporary session data is wiped.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript