# How to integrate Appointo MCP with LlamaIndex

```json
{
  "title": "How to integrate Appointo MCP with LlamaIndex",
  "toolkit": "Appointo",
  "toolkit_slug": "appointo",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/appointo/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/appointo/framework/llama-index.md",
  "updated_at": "2026-05-12T10:01:49.784Z"
}
```

## Introduction

This guide walks you through connecting Appointo to LlamaIndex using the Composio tool router. By the end, you'll have a working Appointo agent that can list all upcoming appointments for today, reschedule a customer booking to next week, create a new appointment for a client through natural language commands.
This guide will help you understand how to give your LlamaIndex agent real control over a Appointo account through Composio's Appointo MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Appointo with

- [OpenAI Agents SDK](https://composio.dev/toolkits/appointo/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/appointo/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/appointo/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/appointo/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/appointo/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/appointo/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/appointo/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/appointo/framework/cli)
- [Google ADK](https://composio.dev/toolkits/appointo/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/appointo/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/appointo/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/appointo/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/appointo/framework/crew-ai)

## 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 Appointo
- Connect LlamaIndex to the Appointo MCP server
- Build a Appointo-powered agent using LlamaIndex
- Interact with Appointo 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 Appointo MCP server, and what's possible with it?

The Appointo MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Appointo account. It is designed to provide structured and secure access to your Appointo appointment booking system, enabling your agent to interact with scheduling and booking operations for your Shopify store. However, at this time, no actionable tools are available for agents to perform automated tasks through the MCP server.
- Appointment management integration: Connect your AI agent to your Appointo account for potential future automation of appointment creation, rescheduling, or cancellations.
- Booking synchronization: Lay the groundwork for syncing booking data between your Shopify store and AI-driven assistants as more features become available.
- Real-time schedule access: Prepare your AI agent to fetch, review, or display booking schedules (when supported by future Appointo tools).
- Customer assistance automation: Enable your agent to support customers with inquiries about appointments, once actionable capabilities are added.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `APPOINTO_AUTHENTICATE` | Authenticate Appointo API Token | Tool to authenticate with the Appointo API by validating the APPOINTO-TOKEN header. Use when verifying the token before subsequent API calls. |
| `APPOINTO_CANCEL_BOOKING` | Cancel Booking | Tool to cancel a booking or selected customers. Use when you need to revoke an entire booking or individual attendees after verifying booking details. Use after retrieving booking information. |
| `APPOINTO_CREATE_BOOKING` | Create Booking | Tool to create a new booking. Use when scheduling a customer booking after confirming appointment availability. |
| `APPOINTO_GET_APPOINTMENT_AVAILABILITY` | Get Appointment Availability | Tool to get calendar availability for a specific appointment. Use when you need to fetch available time slots for an appointment within a date range. |
| `APPOINTO_LIST_APPOINTMENTS` | List Appointments | Tool to list appointments. Use when fetching appointments with optional filters and pagination after confirming a valid API token. |
| `APPOINTO_LIST_BOOKINGS` | List Bookings | Tool to list bookings. Use when you need to fetch bookings filtered by status, paging, or search term. |
| `APPOINTO_LIST_PRODUCTS` | List Products | Tool to fetch all available products. Use when browsing products with optional paging and search. Call after authenticating with Appointo API. |
| `APPOINTO_LIST_SUBSCRIPTION_CONTRACTS` | List Subscription Contracts | Tool to list subscription contracts. Use when retrieving contracts with optional search after confirming a valid API token. |
| `APPOINTO_RESCHEDULE_BOOKING` | Reschedule Booking | Tool to reschedule an existing booking to a new timeslot. Use after confirming new timeslot availability. |
| `APPOINTO_UPDATE_BOOKING` | Update Booking Buffers | Tool to update buffer times for an existing booking. Use after confirming new buffer durations. |
| `APPOINTO_UPSERT_APPOINTMENT_CONFIG` | Upsert Appointment Configuration | Tool to upsert availability config for an appointment. Use when setting or updating appointment availability settings. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Appointo MCP server is an implementation of the Model Context Protocol that connects your AI agent to Appointo. It provides structured and secure access so your agent can perform Appointo operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. 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 Appointo account and project
- Basic familiarity with async Python/Typescript

### 1. Getting API Keys for OpenAI, Composio, and Appointo

No description provided.

### 2. Installing dependencies

No description provided.
```python
pip install composio-llamaindex llama-index llama-index-llms-openai llama-index-tools-mcp python-dotenv
```

```typescript
npm install @composio/llamaindex @llamaindex/openai @llamaindex/tools @llamaindex/workflow dotenv
```

### 3. Set environment variables

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

### 4. Import modules

No description provided.
```python
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()
```

```typescript
import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();
```

### 5. Load environment variables and initialize Composio

No description provided.
```python
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")
```

```typescript
const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!COMPOSIO_API_KEY) throw new Error("COMPOSIO_API_KEY is not set");
if (!COMPOSIO_USER_ID) throw new Error("COMPOSIO_USER_ID is not set");
```

### 6. Create a Tool Router session and build the agent function

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, appointo)
- 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 Appointo tools.
- The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.
```python
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=["appointo"],
    )

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

```typescript
async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["appointo"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
        description : "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform Appointo actions." ,
    llm,
    tools,
  });

  return agent;
}
```

### 7. Create an interactive chat loop

No description provided.
```python
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}")
```

```typescript
async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}
```

### 8. Define the main entry point

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 Appointo
```python
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!")
```

```typescript
async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err) {
    console.error("Failed to start agent:", err);
    process.exit(1);
  }
}

main();
```

### 9. Run the agent

When prompted, authenticate and authorise your agent with Appointo, then start asking questions.
```bash
python llamaindex_agent.py
```

```typescript
npx ts-node llamaindex-agent.ts
```

## Complete Code

```python
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=["appointo"],
    )

    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 Appointo actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Appointo 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!")
```

```typescript
import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";
import { LlamaindexProvider } from "@composio/llamaindex";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();

const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) {
    throw new Error("OPENAI_API_KEY is not set in the environment");
  }
if (!COMPOSIO_API_KEY) {
    throw new Error("COMPOSIO_API_KEY is not set in the environment");
  }
if (!COMPOSIO_USER_ID) {
    throw new Error("COMPOSIO_USER_ID is not set in the environment");
  }

async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["appointo"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
    description:
      "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform Appointo actions." ,
    llm,
    tools,
  });

  return agent;
}

async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}

async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err: any) {
    console.error("Failed to start agent:", err?.message ?? err);
    process.exit(1);
  }
}

main();
```

## Conclusion

You've successfully connected Appointo to LlamaIndex through Composio's Tool Router MCP layer.
Key takeaways:
- Tool Router dynamically exposes Appointo 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 Appointo MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/appointo/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/appointo/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/appointo/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/appointo/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/appointo/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/appointo/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/appointo/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/appointo/framework/cli)
- [Google ADK](https://composio.dev/toolkits/appointo/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/appointo/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/appointo/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/appointo/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/appointo/framework/crew-ai)

## Related Toolkits

- [Google Calendar](https://composio.dev/toolkits/googlecalendar) - Google Calendar is a time management service for scheduling meetings, events, and reminders. It streamlines personal and team organization with integrated notifications and sharing options.
- [Apaleo](https://composio.dev/toolkits/apaleo) - Apaleo is a cloud-based property management platform for hospitality businesses. It centralizes reservations, billing, and daily operations for smoother hotel management.
- [Bart](https://composio.dev/toolkits/bart) - Bart is the Bay Area Rapid Transit system, providing fast public transportation across the San Francisco Bay Area. It helps commuters and travelers get real-time schedule info, plan routes, and stay updated on service changes.
- [Bookingmood](https://composio.dev/toolkits/bookingmood) - Bookingmood is commission-free booking software for rental businesses. It lets you manage reservations and sync bookings directly on your website.
- [Booqable](https://composio.dev/toolkits/booqable) - Booqable is a rental software platform for managing inventory, bookings, and reservations. It helps businesses streamline rentals and keep track of every item with ease.
- [Cal](https://composio.dev/toolkits/cal) - Cal is a meeting scheduling platform that offers shareable booking links and real-time calendar syncing. It streamlines the process of finding mutual availability to make scheduling effortless.
- [Calendarhero](https://composio.dev/toolkits/calendarhero) - Calendarhero is a powerful scheduling platform that streamlines your calendar management across multiple services. It helps you efficiently schedule, reschedule, and organize meetings without the back-and-forth.
- [Calendly](https://composio.dev/toolkits/calendly) - Calendly is an appointment scheduling tool that automates meeting invitations, availability checks, and reminders. It helps individuals and teams avoid endless email back-and-forth when booking meetings.
- [Etermin](https://composio.dev/toolkits/etermin) - eTermin is an online appointment scheduling platform for businesses to manage bookings. It streamlines client appointments, saving time and reducing scheduling conflicts.
- [Evenium](https://composio.dev/toolkits/evenium) - Evenium is an all-in-one platform for managing professional events, from planning to analysis. It helps teams simplify event logistics, boost engagement, and track every detail in one place.
- [Eventee](https://composio.dev/toolkits/eventee) - Eventee is a user-friendly event management platform for mobile and web. It boosts attendee engagement for in-person, virtual, and hybrid events.
- [Eventzilla](https://composio.dev/toolkits/eventzilla) - Eventzilla is an event management platform for creating, promoting, and running events. It streamlines ticketing, registration, and attendee coordination for organizers.
- [Humanitix](https://composio.dev/toolkits/humanitix) - Humanitix is a not-for-profit ticketing platform that donates 100% of profits to charity. It empowers event organizers to make social impact with every ticket sold.
- [Lodgify](https://composio.dev/toolkits/lodgify) - Lodgify is an all-in-one vacation rental software for property managers and owners. It centralizes bookings, guest messaging, and channel synchronization in one dashboard.
- [Planyo Online Booking](https://composio.dev/toolkits/planyo_online_booking) - Planyo Online Booking is a flexible reservation system for managing bookings by day, hour, or event. It streamlines scheduling for any business needing reservations.
- [Scheduleonce](https://composio.dev/toolkits/scheduleonce) - Scheduleonce is a scheduling platform for capturing, qualifying, and engaging with inbound leads. It streamlines appointment booking and follow-ups for faster lead conversion.
- [Supersaas](https://composio.dev/toolkits/supersaas) - Supersaas is a flexible appointment scheduling platform for businesses and individuals. It streamlines bookings, reminders, and calendar management in one place.
- [Sympla](https://composio.dev/toolkits/sympla) - Sympla is a platform for managing in-person and online events, ticket sales, and registrations. It streamlines event setup, attendee tracking, and digital content delivery.
- [Gmail](https://composio.dev/toolkits/gmail) - Gmail is Google's email service with powerful spam protection, search, and G Suite integration. It keeps your inbox organized and makes communication fast and reliable.
- [Google Drive](https://composio.dev/toolkits/googledrive) - Google Drive is a cloud storage platform for uploading, sharing, and collaborating on files. It's perfect for keeping your documents accessible and organized across devices.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Appointo MCP?

With a standalone Appointo MCP server, the agents and LLMs can only access a fixed set of Appointo tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Appointo 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 Appointo tools.

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

Yes, absolutely. You can configure which Appointo 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 Appointo data and credentials are handled as safely as possible.

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[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)