How to Use the Fantastical MCP in LlamaIndex

Index your schedule into LlamaIndex to query your past and future commitments using semantic search.

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Connect Fantastical MCP to LlamaIndex

Create your Vinkius account to connect Fantastical 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 Fantastical with LlamaIndex

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Semantic calendar search via LlamaIndex

Turn your calendar history into a searchable knowledge base. By indexing the results of `list_events`, your agent recalls past meetings and project timelines instantly. It finds patterns in your time usage that traditional search misses. Ask what you worked on last Tuesday and get a grounded answer from your actual data.

Ground your RAG pipeline in live events

Connect your documents to your schedule by passing tool outputs into your vector store. Your agent uses `get_event` to pull context for specific tasks during RAG retrieval. This keeps your AI grounded in reality rather than hallucinating your availability. It knows exactly when you are free because it queries the server directly.

Manage your availability as a data source

Use `list_openings` to treat your scheduling pages as live data points. LlamaIndex ingests these openings to inform the agent's decision-making process. Your agent now understands your schedule as a dynamic index. It makes smarter decisions about when to propose new meetings based on your current constraints.

Setup guide

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

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Common questions about Fantastical MCP in LlamaIndex

Configure the MCP client to pass tool outputs into your index. LlamaIndex then treats your calendar entries as documents for your agent to search.

Yes, by pulling event lists over a defined range. Your agent indexes these events to provide context-aware answers about your past work.

Your calendar events, proposal details, and reminder lists are only accessed during the indexing process. No data is stored permanently outside your control.

It provides a source of truth for your time-based data. By querying the API, the agent avoids guessing your schedule and works from live facts.

The agent is limited by the data returned by the server. If an event isn't in your calendar, the agent won't see it in the index.

Use it with your favorite AI tools

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