How to Use the Cronofy MCP in LlamaIndex

Q: How can I use Cronofy and LlamaIndex to search my calendar?

Use McpToolSpec to load the Cronofy tools into your LlamaIndex agent. Have the agent run readevents, and the output will be automatically indexed. You can then query your meeting history using natural language.

Q: Can I find available meeting times with Cronofy and LlamaIndex?

Yes, your agent can call queryavailability for live data. The unique part is that you can also index the results from getfreebusy over time to analyze availability patterns, not just find the next open slot.

Q: Does LlamaIndex store my Cronofy event data?

LlamaIndex will index the output from Cronofy tools like readevents into your specified vector store. You control where that index lives, whether it's local or in a cloud database. The data is yours to manage.

Turn your calendar history into a searchable knowledge base with Cronofy and LlamaIndex.

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Connect Cronofy MCP to LlamaIndex

Create your Vinkius account to connect Cronofy 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 Cronofy with LlamaIndex

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Index your availability patterns

This server lets you pull availability data with `get_free_busy`. With LlamaIndex, you don't just use that data once. You index it. Your agent can now answer questions by querying a knowledge base of your past and future availability. Instead of hitting the API every time, your agent performs a semantic search over the indexed data. This allows you to ask more complex questions, like "What were my busiest days last quarter?" or "Am I usually free on Friday afternoons?"

Create a queryable event history

Use the `read_events` tool to pull your entire calendar history. LlamaIndex ingests this information and turns it into a vector index. Now you have a private, searchable history of every meeting. Your RAG application can now answer questions grounded in actual data. Ask "What was that meeting about with the marketing team in May?" and get an answer based on the real event details, not a guess from a generic model.

Build a self-aware LlamaIndex agent

Your agent can index its own configuration. Use `get_account` and `list_profiles` to fetch the current setup, then have LlamaIndex add that to its knowledge base. The agent now knows its own context. This means you can ask your agent questions about its own setup, like "Which profiles are you currently configured with?" It queries its own index to answer, making your application more transparent and easier to manage. It's a simple way to make your agent's state part of its knowledge.

Setup guide

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

Use `McpToolSpec` to load the Cronofy tools into your LlamaIndex agent. Have the agent run `read_events`, and the output will be automatically indexed. You can then query your meeting history using natural language.

Yes, your agent can call `query_availability` for live data. The unique part is that you can also index the results from `get_free_busy` over time to analyze availability patterns, not just find the next open slot.

LlamaIndex will index the output from Cronofy tools like `read_events` into your specified vector store. You control where that index lives, whether it's local or in a cloud database. The data is yours to manage.

A standard API call gets you data for one-time use. LlamaIndex takes the data from this MCP Server and makes it a persistent, searchable asset. You're building a knowledge base, not just making a request.

The Cronofy MCP server handles the API connection securely, processing only calendar event and profile data. That data is then passed to your LlamaIndex application. You are in complete control of the vector store where the data is indexed and stored.

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