How to integrate Calendly MCP with LlamaIndex

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Introduction

This guide walks you through connecting Calendly to LlamaIndex using the Composio tool router. By the end, you'll have a working Calendly agent that can create a single-use scheduling link for my next meeting, cancel my 2pm event with a reason, mark an invitee as no-show for today's appointment, delete all invitee data for privacy compliance through natural language commands.

This guide will help you understand how to give your LlamaIndex agent real control over a Calendly account through Composio's Calendly MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

TL;DR

Here's what you'll learn:
  • Set your OpenAI and Composio API keys
  • Install LlamaIndex and Composio packages
  • Create a Composio Tool Router session for Calendly
  • Connect LlamaIndex to the Calendly MCP server
  • Build a Calendly-powered agent using LlamaIndex
  • Interact with Calendly through natural language

What is LlamaIndex?

LlamaIndex is a data framework for building LLM applications. It provides tools for connecting LLMs to external data sources and services through agents and tools.

Key features include:

  • ReAct Agent: Reasoning and acting pattern for tool-using agents
  • MCP Tools: Native support for Model Context Protocol
  • Context Management: Maintain conversation context across interactions
  • Async Support: Built for async/await patterns

What is the Calendly MCP server, and what's possible with it?

The Calendly MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Calendly account. It provides structured and secure access to your scheduling workflows, so your agent can perform actions like creating personalized scheduling links, managing events, handling invitee statuses, and automating reminders on your behalf.

  • Instant scheduling link creation: Direct your agent to generate single-use or shareable scheduling links so others can book time with you instantly—no more back-and-forth emails.
  • Automated event and invitee management: Have your agent cancel events, mark invitees as no-shows, or remove no-show statuses to keep your calendar accurate and up to date.
  • Custom one-off event setup: Empower your agent to create unique, one-off meeting types for special situations, bypassing your regular availability rules.
  • Webhook subscription automation: Let the agent set up webhook subscriptions to trigger notifications or workflows in real time when events happen in your Calendly account.
  • Data privacy and compliance actions: Instruct your agent to delete invitee data or scheduled event records as needed for privacy or regulatory compliance, especially for enterprise use cases.

Supported Tools & Triggers

Tools
Cancel eventPermanently cancels an existing, active scheduled event by its `uuid`, optionally providing a `reason`, which may trigger notifications to invitees.
Create an invitee no-showMarks an invitee, identified by their existing and valid uri, as a 'no show' for a scheduled event.
Create One-Off Event TypeCreates a temporary calendly one-off event type for unique meetings outside regular availability, requiring valid host/co-host uris, a future date/range for `date setting`, and a positive `duration`.
Create scheduling linkCreate a single-use scheduling link.
Create shareCreates a customizable, one-time share link for a calendly event type, allowing specific overrides to its settings (e.
Create single use scheduling linkCreates a one-time, single-use scheduling link for an active calendly event type, expiring after one booking.
Create webhook subscriptionCreates a calendly webhook subscription to notify a specified `url` (which must be a publicly accessible https endpoint) for selected `events` within a given `organization` and `scope`.
Delete invitee dataPermanently removes all invitee data associated with the provided emails from past organization events, for data privacy compliance (requires enterprise subscription; deletion may take up to one week).
Delete invitee no showDeletes an invitee no-show record by its `uuid` to reverse an invitee's 'no-show' status; the `uuid` must refer to an existing record.
Delete scheduled event dataFor enterprise users, initiates deletion of an organization's scheduled event data between a `start time` and `end time` (inclusive, where `start time` must be <= `end time`); actual data deletion may take up to 7 days to complete.
Delete webhook subscriptionDeletes an existing webhook subscription to stop calendly sending event notifications to its registered callback url; this operation is idempotent.
Get current userRetrieves detailed information about the currently authenticated calendly user.
Get eventUse to retrieve a specific calendly scheduled event by its uuid, provided the event exists in the user's calendly account.
Get event inviteeRetrieves detailed information about a specific invitee of a scheduled event, using their unique uuids.
Get event typeRetrieves details for a specific calendly event type, identified by its uuid, which must be valid and correspond to an existing event type.
Get groupRetrieves all attributes of a specific calendly group by its uuid; the group must exist.
Get group relationshipRetrieves a specific calendly group relationship by its valid and existing uuid, providing details on user-group associations and membership.
Get invitee no showRetrieves details for a specific invitee no show record by its uuid; an invitee no show is marked when an invitee does not attend a scheduled event.
Get organization invitationRetrieves a specific calendly organization invitation using its uuid and the parent organization's uuid.
Get organization membershipRetrieves a specific calendly organization membership by its uuid, returning all its attributes.
Get routing formRetrieves a specific routing form by its uuid, providing its configuration details including questions and routing logic.
Get userRetrieves comprehensive details for an existing calendly user.
Get user availability scheduleRetrieves an existing user availability schedule by its uuid; this schedule defines the user's default hours of availability.
Get webhook subscriptionRetrieves the details of an existing webhook subscription, identified by its uuid, including its callback url, subscribed events, scope, and state.
Invite user to organizationInvites a user to the specified calendly organization by email, if they aren't already a member and lack a pending invitation to it.
List activity log entriesRetrieves a list of activity log entries for a specified calendly organization (requires an active enterprise subscription), supporting filtering, sorting, and pagination.
List event inviteesRetrieves a list of invitees for a specified calendly event uuid, with options to filter by status or email, and sort by creation time.
List eventsRetrieves a list of scheduled calendly events; requires `user`, `organization`, `group`, or `invitee email` for scope, and admin rights may be needed when filtering by `organization` or `group`.
List event type available timesFetches available time slots for a calendly event type within a specified time range; results are not paginated.
List event type hostsRetrieves a list of hosts (users) assigned to a specific, existing calendly event type, identified by its uri.
List group relationshipsRetrieves a list of group relationships defining an owner's role (e.
List groupsReturns a list of groups for a specified calendly organization uri, supporting pagination.
List organization invitationsRetrieves a list of invitations for a specific organization, identified by its uuid.
List organization membershipsRetrieves a list of organization memberships.
List outgoing communicationsRetrieves a list of outgoing sms communications for a specified organization; requires an enterprise subscription and if filtering by creation date, both `min created at` and `max created at` must be provided to form a valid range.
List routing formsRetrieves routing forms for a specified organization; routing forms are questionnaires used to direct invitees to appropriate booking pages or external urls.
List user availability schedulesRetrieves all availability schedules for the specified calendly user.
List user busy timesFetches a user's busy time intervals (internal and external calendar events) in ascending order for a period up to 7 days; keyset pagination is not supported.
List user event typesRetrieves event types for a user or organization; requires either the `user` or `organization` uri.
List webhook subscriptionsRetrieves webhook subscriptions for a calendly organization; `scope` determines if `user` or `group` uri is also required for filtering.
Remove user from organizationRemoves a user (who is not an owner) from an organization by their membership uuid, requiring administrative privileges.
Revoke a user's organization invitationRevokes a pending and revokable (not yet accepted or expired) organization invitation using its uuid and the organization's uuid, rendering the invitation link invalid.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before you begin, make sure you have:
  • Python 3.8/Node 16 or higher installed
  • A Composio account with the API key
  • An OpenAI API key
  • A Calendly account and project
  • Basic familiarity with async Python/Typescript

Getting API Keys for OpenAI, Composio, and Calendly

OpenAI API key (OPENAI_API_KEY)
  • Go to the OpenAI dashboard
  • Create an API key if you don't have one
  • Assign it to OPENAI_API_KEY in .env
Composio API key and user ID
  • Log into the Composio dashboard
  • Copy your API key from Settings
    • Use this as COMPOSIO_API_KEY
  • Pick a stable user identifier (email or ID)
    • Use this as COMPOSIO_USER_ID

Installing dependencies

pip install composio-llamaindex llama-index llama-index-llms-openai llama-index-tools-mcp python-dotenv

Create a new Python project and install the necessary dependencies:

  • composio-llamaindex: Composio's LlamaIndex integration
  • llama-index: Core LlamaIndex framework
  • llama-index-llms-openai: OpenAI LLM integration
  • llama-index-tools-mcp: MCP client for LlamaIndex
  • python-dotenv: Environment variable management

Set environment variables

bash
OPENAI_API_KEY=your-openai-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_USER_ID=your-user-id

Create a .env file in your project root:

These credentials will be used to:

  • Authenticate with OpenAI's GPT-5 model
  • Connect to Composio's Tool Router
  • Identify your Composio user session for Calendly access

Import modules

import asyncio
import os
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

Create a new file called calendly_llamaindex_agent.py and import the required modules:

Key imports:

  • asyncio: For async/await support
  • Composio: Main client for Composio services
  • LlamaIndexProvider: Adapts Composio tools for LlamaIndex
  • ReActAgent: LlamaIndex's reasoning and action agent
  • BasicMCPClient: Connects to MCP endpoints
  • McpToolSpec: Converts MCP tools to LlamaIndex format

Load environment variables and initialize Composio

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set in the environment")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment")

What's happening:

This ensures missing credentials cause early, clear errors before the agent attempts to initialise.

Create a Tool Router session and build the agent function

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["calendly"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")

    description = "An agent that uses Composio Tool Router MCP tools to perform Calendly actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Calendly actions.
    """
    return ReActAgent(tools=tools, llm=llm, description=description, system_prompt=system_prompt, verbose=True)

What's happening here:

  • We create a Composio client using your API key and configure it with the LlamaIndex provider
  • We then create a tool router MCP session for your user, specifying the toolkits we want to use (in this case, calendly)
  • The session returns an MCP HTTP endpoint URL that acts as a gateway to all your configured tools
  • LlamaIndex will connect to this endpoint to dynamically discover and use the available Calendly tools.
  • The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.

Create an interactive chat loop

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

What's happening here:

  • We're creating a direct terminal interface to chat with your Calendly database
  • The LLM's responses are streamed to the CLI for faster interaction.
  • The agent uses context to maintain conversation history
  • You can type 'quit' or 'exit' to stop the chat loop gracefully
  • Agent responses and any errors are displayed in a clear, readable format

Define the main entry point

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")

What's happening here:

  • We're orchestrating the entire application flow
  • The agent gets built with proper error handling
  • Then we kick off the interactive chat loop so you can start talking to Calendly

Run the agent

npx ts-node llamaindex-agent.ts

When prompted, authenticate and authorise your agent with Calendly, then start asking questions.

Complete Code

Here's the complete code to get you started with Calendly and LlamaIndex:

import asyncio
import os
import signal
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["calendly"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")
    description = "An agent that uses Composio Tool Router MCP tools to perform Calendly actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Calendly actions.
    """
    return ReActAgent(
        tools=tools,
        llm=llm,
        description=description,
        system_prompt=system_prompt,
        verbose=True,
    );

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")

Conclusion

You've successfully connected Calendly to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways:
  • Tool Router dynamically exposes Calendly tools through an MCP endpoint
  • LlamaIndex's ReActAgent handles reasoning and orchestration; Composio handles integrations
  • The agent becomes more capable without increasing prompt size
  • Async Python provides clean, efficient execution of agent workflows
You can easily extend this to other toolkits like Gmail, Notion, Stripe, GitHub, and more by adding them to the toolkits parameter.

How to build Calendly MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Calendly MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Calendly MCP with Claude Agent SDK

Google ADK

Google ADK

Use Calendly MCP with Google ADK

LangChain

LangChain

Use Calendly MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Calendly MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Calendly MCP with Mastra AI

CrewAI

CrewAI

Use Calendly MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Calendly MCP?

With a standalone Calendly MCP server, the agents and LLMs can only access a fixed set of Calendly tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Calendly and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with LlamaIndex?

Yes, you can. LlamaIndex fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Calendly tools.

Can I manage the permissions and scopes for Calendly while using Tool Router?

Yes, absolutely. You can configure which Calendly scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Calendly data and credentials are handled as safely as possible.

Used by agents from

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