How to Use the World Time (Keyless) MCP in LlamaIndex

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Connect World Time (Keyless) MCP to LlamaIndex

Create your Vinkius account to connect World Time (Keyless) 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 World Time (Keyless) with LlamaIndex

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LlamaIndex: Indexing Timezone Data

Don't just run a query; make the result searchable. When your LlamaIndex agent calls `get_current_time`, the resulting timestamp and timezone data are indexed into your vector store. You can then query past sessions using this real-world context. This means you can ask, 'What was the UTC offset in Europe/London last Tuesday?' and get a grounded answer from the API output stored in your index.

MCP Server: Querying World Time Zones via LlamaIndex

Using `list_available_timezones`, you populate your knowledge base with metadata about global time zones. You can build a unified index that maps timezone strings to their location and characteristics. This allows the RAG application built in LlamaIndex to answer complex questions like, 'List all Asian time zones that observe DST.' The tool output becomes structured, retrievable knowledge.

LlamaIndex: Inferring Time from IP Addresses

When an incoming document or query mentions an IP address, LlamaIndex can use the `get_current_time` tool to resolve the location and current time. This resolved data point is then indexed alongside the original text. This enriches your knowledge base significantly. You're not just storing text; you're storing contextually rich, geo-timed records that improve semantic search accuracy.

Setup guide

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

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Common questions about World Time (Keyless) MCP in LlamaIndex

The tool output becomes a source of truth for your index. Instead of relying on the LLM's memory, you are grounding answers in the precise atomic time data returned by the MCP Server.

No. You simply define the tool spec and pass it to your FunctionAgent. The agent handles calling `get_current_time` as part of its reasoning process.

No, this server provides the current atomic time and DST status. The index only stores snapshots of that live data when you run a retrieval job.

It exposes structured data: precise timestamps, UTC offsets, and the DST status for any given timezone string.

You are working with time zone metadata. This includes standard geographical region identifiers and precise atomic timestamps.

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