How to Use the TimezoneDB MCP in LlamaIndex

Q: How do I filter tools for TimezoneDB in LlamaIndex?

Use the allowedtools filter during agent initialization. This restricts the agent to only the specific time lookup tools you want to expose.

Index global time data into LlamaIndex. Ground your RAG applications with live, accurate offsets via the TimezoneDB MCP Server.

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Setup TimezoneDB with LlamaIndex

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Claude

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Vectorize temporal context

Feed results from `get_time_by_zone` into your LlamaIndex knowledge base. It turns raw time data into searchable context for your RAG documents. Your agents answer questions grounded in current reality. They no longer hallucinate offsets because they query the live API as needed.

Location-aware retrieval

Use `get_time_by_location` to pull context based on geographic coordinates. LlamaIndex then indexes this result for future semantic retrieval. Your system understands time-based triggers for specific regions. This creates a bridge between static documents and dynamic local time.

Audit your global timezones

Query `list_timezones` to populate your index with valid regional data. It ensures your agent knows exactly which zones are available for lookup. Keep your index updated with the latest API definitions. It removes the need for hardcoded, stale timezone lists in your application logic.

Setup guide

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

Use the basic MCP client to define your server connection. Import the tool spec and pass it to your function agent to start indexing.

It can. Once the tools are part of your agent's tool list, LlamaIndex decides when to retrieve live time data to support its answers.

It will. By grounding your responses in live API data, you reduce errors regarding DST and local time offsets significantly.

Use the allowed_tools filter during agent initialization. This restricts the agent to only the specific time lookup tools you want to expose.

The server transmits only the requested timezone or coordinate data. Your local index stores these results securely, separated from your API credentials.

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