# How to integrate Coassemble MCP with LlamaIndex

```json
{
  "title": "How to integrate Coassemble MCP with LlamaIndex",
  "toolkit": "Coassemble",
  "toolkit_slug": "coassemble",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/coassemble/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/coassemble/framework/llama-index.md",
  "updated_at": "2026-05-12T10:06:53.950Z"
}
```

## Introduction

This guide walks you through connecting Coassemble to LlamaIndex using the Composio tool router. By the end, you'll have a working Coassemble agent that can list all active courses for your team, show learner progress for a specific course, find users who haven't completed training through natural language commands.
This guide will help you understand how to give your LlamaIndex agent real control over a Coassemble account through Composio's Coassemble MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Coassemble with

- [OpenAI Agents SDK](https://composio.dev/toolkits/coassemble/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/coassemble/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/coassemble/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/coassemble/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/coassemble/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/coassemble/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/coassemble/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/coassemble/framework/cli)
- [Google ADK](https://composio.dev/toolkits/coassemble/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/coassemble/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/coassemble/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/coassemble/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/coassemble/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 Coassemble
- Connect LlamaIndex to the Coassemble MCP server
- Build a Coassemble-powered agent using LlamaIndex
- Interact with Coassemble 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 Coassemble MCP server, and what's possible with it?

The Coassemble MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Coassemble account. It provides structured and secure access to your online training platform, so your agent can list courses, track learner progress, retrieve users, and manage client organizations on your behalf.
- Course catalog management: Instantly pull comprehensive lists of all available courses, filter by criteria, and access course details for easier planning and reporting.
- Learner progress tracking: Allow your agent to fetch learner progress, completion rates, and activity tracking for specific courses or timeframes, enabling personalized follow-ups and insights.
- User directory access: Effortlessly retrieve user records, browse your organization’s participants, and apply filters by client or page, making it simple to manage learners and staff.
- Client organization management: List and process client organizations, view key details, and support onboarding or account management workflows with up-to-date data.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `COASSEMBLE_GET_CLIENTS` | Get Clients | Retrieve a paginated list of clients (organisations). Supports pagination via 'length' and 'page' parameters. Use when you need to list all client organisations or page through them systematically. |
| `COASSEMBLE_GET_COURSES` | Get Courses | Retrieve all courses from your Coassemble account with optional filtering and pagination. Use this tool to list, search, or browse courses. You can filter by user identifier, client identifier, or title, and control pagination with length and page parameters. All parameters are optional - call without parameters to get all courses. |
| `COASSEMBLE_GET_TRACKINGS` | Get Trackings | Retrieves learner progress tracking records for a specific course. Returns tracking objects containing progress percentages, completion status, and timestamps. Supports filtering by learner identifier, client identifier, date range, and completion status, with pagination support. |
| `COASSEMBLE_GET_USERS` | Get Users | Tool to retrieve a paginated list of users. Use when you need to browse or process user records optionally filtered by clientIdentifier. |

## Supported Triggers

None listed.

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

The Coassemble MCP server is an implementation of the Model Context Protocol that connects your AI agent to Coassemble. It provides structured and secure access so your agent can perform Coassemble 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 Coassemble account and project
- Basic familiarity with async Python/Typescript

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

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 Coassemble 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, coassemble)
- 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 Coassemble 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=["coassemble"],
    )

    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 Coassemble actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Coassemble 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: ["coassemble"],
    },
  );

  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 Coassemble 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 Coassemble
```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 Coassemble, 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=["coassemble"],
    )

    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 Coassemble actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Coassemble 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: ["coassemble"],
    },
  );

  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 Coassemble 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 Coassemble to LlamaIndex through Composio's Tool Router MCP layer.
Key takeaways:
- Tool Router dynamically exposes Coassemble 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 Coassemble MCP Agent with another framework

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

## Related Toolkits

- [Canvas](https://composio.dev/toolkits/canvas) - Canvas is a learning management system for online courses, assignments, grading, and collaboration. It's trusted by educators and students to streamline virtual classrooms and enhance digital learning.
- [Accredible certificates](https://composio.dev/toolkits/accredible_certificates) - Accredible Certificates is a platform for creating and managing digital certificates, badges, and blockchain credentials. It streamlines issuing, tracking, and verifying professional achievements for organizations of any size.
- [Api bible](https://composio.dev/toolkits/api_bible) - API.Bible is a developer platform for Scripture content and passage search. Easily integrate Bible verses and translations into your apps or chatbots.
- [Blackboard](https://composio.dev/toolkits/blackboard) - Blackboard is a digital learning platform for higher education and schools, offering tools to manage courses, track engagement, and deliver interactive content. It helps institutions improve student outcomes through actionable analytics and in-app guidance.
- [Certifier](https://composio.dev/toolkits/certifier) - Certifier is a platform for creating, managing, and issuing digital certificates and credentials. Organizations use it to automate and secure the entire credentialing process.
- [Classmarker](https://composio.dev/toolkits/classmarker) - ClassMarker is a professional online quiz maker for business and education. It provides instant grading, flexible test design, and in-depth reporting.
- [D2lbrightspace](https://composio.dev/toolkits/d2lbrightspace) - D2L Brightspace is a learning management system for delivering and managing online courses and assessments. It helps educators streamline digital teaching, assignments, and communication with students.
- [Dictionary api](https://composio.dev/toolkits/dictionary_api) - Dictionary api is the Merriam-Webster API providing rich dictionary and thesaurus data for developers. Instantly access definitions, synonyms, etymologies, and audio pronunciations in your apps.
- [Google Classroom](https://composio.dev/toolkits/google_classroom) - Google Classroom is a free web service for educators and students to manage assignments and communication. It streamlines classroom collaboration and grading, making teaching simpler and more connected.
- [Lessonspace](https://composio.dev/toolkits/lessonspace) - Lessonspace is an online collaborative classroom platform offering video, whiteboards, and real-time interaction for educators and students. It streamlines remote teaching with integrated tools for engagement and communication.
- [Linguapop](https://composio.dev/toolkits/linguapop) - Linguapop is a web platform for administering language placement tests in English, German, Spanish, Italian, and French. It helps schools and organizations efficiently manage multilingual assessments and analyze results.
- [Memberspot](https://composio.dev/toolkits/memberspot) - Memberspot is an online course and video-hosting platform for business learning. It helps teams manage, deliver, and track knowledge efficiently.
- [Membervault](https://composio.dev/toolkits/membervault) - Membervault is a platform for hosting courses, memberships, and digital products in one place. It helps you build stronger relationships with your audience by centralizing digital offers and customer engagement.
- [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 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.
- [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.
- [Outlook](https://composio.dev/toolkits/outlook) - Outlook is Microsoft's email and calendaring platform for unified communications and scheduling. It helps users stay organized with powerful email, contacts, and calendar management.
- [Twitter](https://composio.dev/toolkits/twitter) - Twitter is a social media platform for sharing real-time updates, conversations, and news. Stay connected, informed, and engaged with communities worldwide.
- [Google Sheets](https://composio.dev/toolkits/googlesheets) - Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly.
- [Supabase](https://composio.dev/toolkits/supabase) - Supabase is an open-source backend platform offering scalable Postgres databases, authentication, storage, and real-time APIs. It lets developers build modern apps without managing infrastructure.

## Frequently Asked Questions

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

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

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

Yes, absolutely. You can configure which Coassemble 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 Coassemble 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)