How to Use the Calendly MCP in LlamaIndex

Q: Can I use LlamaIndex to search for past Calendly meetings with a specific person?

Absolutely. First, use listscheduledevents to get your history, then listinvitees for each event. LlamaIndex will index this data, letting you query for meetings by attendee name.

Q: How does LlamaIndex handle finding open slots in Calendly?

Your LlamaIndex agent uses the getavailabletimes tool. It queries the Calendly API for a specific event type and date range, then can index those results or present them directly.

Q: Can I build a RAG app with LlamaIndex that knows my Calendly event types?

Yes, that's a perfect use case. Call listeventtypes to get all your meeting templates. LlamaIndex indexes their details, so you can ask your agent things like "What's the link for my 30-minute intro call?".

Q: How can I get my own user info from Calendly?

The getuser tool is what you need. It fetches your profile details like name, email, and master scheduling URL.

Turn your Calendly history into a searchable knowledge base with LlamaIndex.

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Connect Calendly MCP to LlamaIndex

Create your Vinkius account to connect Calendly 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 Calendly with LlamaIndex

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Create a Searchable Event Index

LlamaIndex makes tool outputs part of a queryable knowledge base. Use the `list_scheduled_events` tool to pull your entire meeting history. LlamaIndex will then index the details of every event, including attendees, times, and types. Now you can ask natural language questions about your past meetings. "Who did I meet with about Project X in May?" Your agent will query the index it built from the Calendly API data to give you a grounded answer, not a guess. It turns your schedule into a database.

Understand Team Availability with RAG

This MCP Server lets you index your entire organization's structure. The `list_org_members` tool pulls in your team data, and `list_availability` can fetch each person's configured work hours. LlamaIndex turns this into a structured, searchable index. You can then build a RAG application to answer complex queries. "Find a 30-minute slot next week for me, Jane, and Bob from the engineering team." Your agent queries the indexed availability data to find a common opening, using real data from the `get_available_times` tool.

Live Data for Your LlamaIndex Agent

Your LlamaIndex agent doesn't have to rely on stale information. It can use the MCP tools to fetch fresh data from Calendly on the fly. Before answering a question, it can call `get_scheduled_event` to make sure the meeting details are still current. This is about grounding your agent's responses in reality. When your agent suggests an available time slot using `get_available_times`, it's because it just checked the Calendly API seconds ago. The answers are reliable because they're based on live data, not just a static index.

Setup guide

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

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Common questions about Calendly MCP in LlamaIndex

Absolutely. First, use `list_scheduled_events` to get your history, then `list_invitees` for each event. LlamaIndex will index this data, letting you query for meetings by attendee name.

Your LlamaIndex agent uses the `get_available_times` tool. It queries the Calendly API for a specific event type and date range, then can index those results or present them directly.

Yes, that's a perfect use case. Call `list_event_types` to get all your meeting templates. LlamaIndex indexes their details, so you can ask your agent things like "What's the link for my 30-minute intro call?".

The `get_user` tool is what you need. It fetches your profile details like name, email, and master scheduling URL.

The MCP server itself doesn't store your data. It acts as a secure pass-through to the Calendly API. Your information, like event details and invitee lists, is processed in a temporary, isolated sandbox for each request and then discarded.

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