How do I list all the holidays for a specific country in 2024?

Simply ask the agent to `list_holidays` and provide the Country code (e.g., 'US' or 'BR') and the year '2024'. It will retrieve the full list from Calendarific.

Can I filter holidays by type, like only religious holidays?

Yes! Use the `list_holidays_by_type` tool and provide the country, year, and type (e.g., 'religious'). Your agent will return only the matching observances.

How do I see which countries are supported by Calendarific?

Use the `list_supported_countries` tool. It will retrieve the complete directory of over 230 countries currently available in the Calendarific API.

Calendarific MCP Server for LlamaIndex 6 tools — connect in under 2 minutes

Name: Calendarific
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 6 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Calendarific as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Calendarific. "
            "You have 6 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Calendarific?"
    )
    print(response)

asyncio.run(main())

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About Calendarific MCP Server

Connect your Calendarific account to any AI agent and orchestrate your global scheduling, vacation planning, and local observability through natural conversation.

LlamaIndex agents combine Calendarific tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

Holiday Oversight — List all public, religious, and local holidays for a specific country and year.
Regional Observability — Filter holidays by state or region to ensure your planning accounts for local variations.
Categorized Discovery — List holidays by type, such as National, Observance, or Religious holidays.
Country & Language Directory — Access the list of 230+ supported countries and their respective ISO codes.
Date-Specific Queries — Retrieve holidays for a specific month or day to verify work availability.
Reference Data — Get detailed metadata for each holiday, including descriptions and observance regions.

The Calendarific MCP Server exposes 6 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Calendarific to LlamaIndex via MCP

Follow these steps to integrate the Calendarific MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 6 tools from Calendarific

Why Use LlamaIndex with the Calendarific MCP Server

LlamaIndex provides unique advantages when paired with Calendarific through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Calendarific tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Calendarific tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Calendarific, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Calendarific tools were called, what data was returned, and how it influenced the final answer

Calendarific + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Calendarific MCP Server delivers measurable value.

Hybrid search: combine Calendarific real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Calendarific to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Calendarific for fresh data

Analytical workflows: chain Calendarific queries with LlamaIndex's data connectors to build multi-source analytical reports

Calendarific MCP Tools for LlamaIndex (6)

These 6 tools become available when you connect Calendarific to LlamaIndex via MCP:

get_account_info

Check the status of the integration

list_holidays

List holidays for a specific country and year

list_holidays_by_location

List holidays for a specific state or region

list_holidays_by_type

List holidays filtered by type (e.g. national, religious)

list_supported_countries

List all supported countries and their ISO codes

list_supported_languages

List all supported languages

Example Prompts for Calendarific in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Calendarific immediately.

"List all public holidays in Brazil for 2024."

"Are there any holidays in New York (US-NY) on December 25th?"

"Show me the religious holidays in Italy for 2024."

Troubleshooting Calendarific MCP Server with LlamaIndex

Common issues when connecting Calendarific to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Calendarific + LlamaIndex FAQ

Common questions about integrating Calendarific MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Calendarific tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.