How to Use the Nager.Date MCP in LlamaIndex

Index global calendar events into your LlamaIndex knowledge base for semantic retrieval and grounded AI responses.

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Connect Nager.Date MCP to LlamaIndex

Create your Vinkius account to connect Nager.Date 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 Nager.Date with LlamaIndex

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Turn holiday data into searchable knowledge

Use `list_available_countries` to populate your vector store with current regional data. LlamaIndex then treats these holidays as factual nodes for your RAG pipeline. Your agent doesn't just read the API once. It indexes the results, making historical and future holiday data queryable alongside your own documents.

Ground answers in live API data

Connect `get_public_holidays` to a FunctionAgent to verify dates against live records. Your application answers questions using verified calendar data instead of training-set assumptions. This approach stops hallucinations. When a user asks about a specific date, the agent fetches the latest info and anchors its response in the actual API output.

Filter tools for secure access

Use the allowed_tools filter to restrict which parts of the Nager.Date API your index can access. You keep your agent focused only on the holiday data that matters for your specific use case. Configuration is straightforward. You define the scope in your McpToolSpec, ensuring the agent only queries the endpoints you explicitly permit.

Setup guide

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

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Common questions about Nager.Date MCP in LlamaIndex

The MCP tools output structured JSON which LlamaIndex converts into text nodes. These nodes are then embedded into your vector store for retrieval.

You can. By passing the tools to a FunctionAgent, the model interprets your question and calls the correct API endpoint to find the answer.

It is persistent if you save your index to disk. The retrieved holiday data becomes a permanent part of your local knowledge base.

Simply trigger a re-run of your ingestion pipeline. The agent will call the tools again, fetch the latest public holidays, and refresh the vector store nodes.

The server provides public, non-sensitive holiday schedules and country codes. No customer data or private account information is handled or stored by this tool.

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