# How to integrate Happy scribe MCP with LlamaIndex

```json
{
  "title": "How to integrate Happy scribe MCP with LlamaIndex",
  "toolkit": "Happy scribe",
  "toolkit_slug": "happy_scribe",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/happy_scribe/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/happy_scribe/framework/llama-index.md",
  "updated_at": "2026-05-12T10:14:37.015Z"
}
```

## Introduction

This guide walks you through connecting Happy scribe to LlamaIndex using the Composio tool router. By the end, you'll have a working Happy scribe agent that can transcribe this podcast episode to text, generate subtitles for uploaded video file, export subtitles in srt format for review through natural language commands.
This guide will help you understand how to give your LlamaIndex agent real control over a Happy scribe account through Composio's Happy scribe MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Happy scribe with

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

The Happy Scribe MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Happy Scribe account. It provides structured and secure access to your transcription and subtitle services, so your agent can perform actions like starting new transcriptions, generating subtitles, exporting files, and managing your transcription jobs on your behalf.
- Automated transcription creation: Instantly start new transcription jobs from audio or video files using a simple agent command.
- Subtitle generation for videos: Have your agent generate accurate subtitles for your video content for accessibility and localization.
- Export and download transcripts or subtitles: Let your agent export completed transcriptions or subtitles in various formats for easy distribution.
- Account and usage monitoring: Retrieve account details, subscription status, and API usage statistics to keep tabs on your service limits.
- Transcription management and cleanup: Direct your agent to delete completed or unwanted transcription jobs, keeping your workspace organized.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `HAPPY_SCRIBE_CREATE_SUBTITLE` | Create Subtitle | Create subtitles for a video file using Happy Scribe's automatic transcription service. Submits a video URL to be processed for subtitle generation. The video must be publicly accessible during ingestion. Returns a subtitle job with an editor URL for reviewing and editing the generated subtitles. Processing states progress from 'initial' -> 'ingesting' -> 'automatic_done' (or 'failed'). Use the GET transcription endpoint to check processing status. |
| `HAPPY_SCRIBE_CREATE_TRANSLATION_TASK` | Create Translation Task | Creates an AI-powered translation task to translate an existing transcription into another language. Use this after a transcription is complete (state='automatic_done') to translate it. The task runs asynchronously - use Retrieve Translation Task to check progress and get results. Note: Not all language pairs are supported (e.g., German to English may fail). |
| `HAPPY_SCRIBE_DELETE_TRANSCRIPTION` | Delete Transcription | Tool to delete a transcription job. Use when you have a transcription ID and want to remove it, optionally permanently. Use after confirming the transcription ID. |
| `HAPPY_SCRIBE_DELETE_WEBHOOK` | Delete Webhook | Deletes a webhook by its ID. This action is idempotent: if the webhook does not exist or was already deleted, it returns success. Use Get Webhooks to retrieve available webhook IDs. |
| `HAPPY_SCRIBE_GET_ACCOUNT_DETAILS` | Get Account Details | Tool to retrieve details about your account, including subscription status and usage statistics. Use after authenticating your API key to monitor plan and usage. |
| `HAPPY_SCRIBE_GET_LANGUAGE_LIST` | Get Supported Languages | Retrieve the list of supported language codes for Happy Scribe transcription services. Returns BCP-47 language codes and indicates which languages have human transcription service available. This is a static reference based on Happy Scribe's official documentation, as there is no dedicated languages API endpoint. |
| `HAPPY_SCRIBE_GET_RATE_LIMIT` | Get API Rate Limit | Get Happy Scribe API rate limit information. Returns the documented rate limit for the Happy Scribe API: - Default limit: 200 requests per hour - Applies only to the transcription creation endpoint - When rate limited, API returns HTTP 429 with 'retry_in_seconds' in the body Note: Happy Scribe does not expose rate limit headers, so this tool provides documented defaults. If you encounter a 429 response during transcription creation, wait for the 'retry_in_seconds' value before retrying. For higher limits, contact sales@happyscribe.co with subject "Happy Scribe for Business". |
| `HAPPY_SCRIBE_GET_SIGNED_UPLOAD_URL` | Get Signed Upload URL | Tool to get a signed URL for uploading a file to Happy Scribe's S3 storage. Use before creating a transcription or order to obtain a secure upload URL for your media file. |
| `HAPPY_SCRIBE_HS_CONFIRM_ORDER` | Confirm Order | Tool to confirm a pending order. Use after creating an order with confirm=false when you're ready to submit it for processing. |
| `HAPPY_SCRIBE_HS_CREATE_TRANSLATION_ORDER` | Create Translation Order | Tool to create a translation order from an existing transcription. Use when you have a completed transcription and want translations into one or more languages. By default, the order remains incomplete unless confirm=true. |
| `HAPPY_SCRIBE_HS_EXPORT_TRANSCRIPTION` | Export Transcription | Creates an export job to download transcription content in various formats. Use this action after a transcription job completes (state='automatic_done'). First obtain transcription IDs using the List Transcriptions action. The export job runs asynchronously - poll the Retrieve Export action with the returned export ID to check when the download URL becomes available (state='ready'). Supported formats: - Documents: txt, docx, pdf (support timestamps, speakers, comments, highlights) - Subtitles: srt, vtt, stl (standard caption formats) - Video editing: avid, premiere, fcp (professional editing software) - Other: html, json, maxqda |
| `HAPPY_SCRIBE_HS_GET_API_VERSION` | Get API Version | Tool to retrieve current API version and check for updates. If Happy Scribe does not expose a dedicated /version endpoint, this tool attempts to infer the version from HTTP response headers or from the base_url path (e.g., /api/v1). |
| `HAPPY_SCRIBE_HS_GET_ERROR_CODES` | Get Error Codes | Returns a list of HTTP error codes used by the Happy Scribe API along with their descriptions. Use this tool to understand what different error responses mean when calling other Happy Scribe API endpoints. This returns static reference data matching the official Happy Scribe API documentation. |
| `HAPPY_SCRIBE_HS_GET_SUPPORTED_FORMATS` | Get Supported Formats | Tool to retrieve supported file formats. Use when you need to know available input and output formats before creating a transcription or subtitle. If a dedicated '/formats' endpoint is unavailable, this action probes known endpoints to verify connectivity and returns a curated list based on official documentation. |
| `HAPPY_SCRIBE_HS_GET_TRANSCRIPTION` | Get Transcription Details | Tool to retrieve details and status of a specific transcription job. Use after creating or listing transcription jobs to inspect a particular job's metadata. |
| `HAPPY_SCRIBE_HS_GET_WEBHOOKS` | Get Webhooks | Tool to retrieve webhooks configured for your account. Note: Happy Scribe's public API does not have a dedicated webhooks listing endpoint. Webhooks are specified via webhook_url when creating orders. This action attempts common endpoints and returns an empty list if unavailable. |
| `HAPPY_SCRIBE_HS_RETRIEVE_EXPORT` | Retrieve Export | Tool to retrieve information about a specific export. Use when you need to check export status and get download link. |
| `HAPPY_SCRIBE_LIST_TRANSCRIPTIONS` | List Transcriptions | Retrieves a paginated list of transcription jobs for a Happy Scribe organization. Returns transcription metadata including ID, name, processing state, language, and duration. Supports filtering by folder and tags, with pagination for large result sets. Note: This returns metadata only; use the Export Transcription action to get actual transcript content. |
| `HAPPY_SCRIBE_RETRIEVE_ORDER` | Retrieve Order | Retrieve details of a Happy Scribe order by its ID. Returns order state, pricing, operations, and inputs. Use this to check order status, verify pricing details, or get information about translation/transcription operations. The order ID is obtained from create order responses (e.g., Create Translation Order). |

## Supported Triggers

None listed.

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

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

### 1. Getting API Keys for OpenAI, Composio, and Happy scribe

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 Happy scribe 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, happy scribe)
- 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 Happy scribe 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=["happy_scribe"],
    )

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

  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 Happy scribe 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 Happy scribe
```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 Happy scribe, 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=["happy_scribe"],
    )

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

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

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

## Related Toolkits

- [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.
- [Google Docs](https://composio.dev/toolkits/googledocs) - Google Docs is a cloud-based word processor that enables document creation and real-time collaboration. Its seamless sharing and version history make team editing and content management a breeze.
- [Google Super](https://composio.dev/toolkits/googlesuper) - Google Super is an all-in-one suite combining Gmail, Drive, Calendar, Sheets, Analytics, and more. It gives you a unified platform to manage your digital life, boosting productivity and organization.
- [Affinda](https://composio.dev/toolkits/affinda) - Affinda is an AI-powered document processing platform that automates data extraction from resumes, invoices, and more. It streamlines document-heavy workflows by turning files into structured, actionable data.
- [Agility cms](https://composio.dev/toolkits/agility_cms) - Agility CMS is a headless content management system for building and managing digital experiences across platforms. It lets teams update content quickly and deliver omnichannel experiences with ease.
- [Algodocs](https://composio.dev/toolkits/algodocs) - Algodocs is an AI-powered platform that automates data extraction from business documents. It delivers fast, secure, and accurate processing without templates or manual training.
- [Api2pdf](https://composio.dev/toolkits/api2pdf) - Api2Pdf is a REST API for generating PDFs from HTML, URLs, and documents using powerful engines like wkhtmltopdf and Headless Chrome. It streamlines document conversion and automation for developers and businesses.
- [Aryn](https://composio.dev/toolkits/aryn) - Aryn is an AI-powered platform for parsing, extracting, and analyzing data from unstructured documents. Use it to automate document processing and unlock actionable insights from your files.
- [Boldsign](https://composio.dev/toolkits/boldsign) - Boldsign is a digital eSignature platform for sending, signing, and tracking documents online. Organizations use it to automate agreements and manage legally binding workflows efficiently.
- [Boloforms](https://composio.dev/toolkits/boloforms) - BoloForms is an eSignature platform built for small businesses, offering unlimited signatures, templates, and forms. It simplifies digital document signing and team collaboration at a predictable, fixed price.
- [Box](https://composio.dev/toolkits/box) - Box is a cloud content management and file sharing platform for businesses. It helps teams securely store, organize, and collaborate on files from anywhere.
- [Carbone](https://composio.dev/toolkits/carbone) - Carbone is a blazing-fast report generator that turns JSON data into PDFs, Word docs, spreadsheets, and more using flexible templates. It lets you automate document creation at scale with minimal code.
- [Castingwords](https://composio.dev/toolkits/castingwords) - CastingWords is a transcription service specializing in human-powered, accurate transcripts via a simple API. Get seamless audio-to-text conversion for interviews, meetings, podcasts, and more.
- [Cloudconvert](https://composio.dev/toolkits/cloudconvert) - CloudConvert is a powerful file conversion service supporting over 200 file formats. It streamlines converting, compressing, and managing documents, media, and more, all in one place.
- [Cloudlayer](https://composio.dev/toolkits/cloudlayer) - Cloudlayer is a document and asset generation service for creating PDFs and images via API or SDKs. It lets you automate high-quality doc creation, saving dev time and reducing manual work.
- [Cloudpress](https://composio.dev/toolkits/cloudpress) - Cloudpress is a content export tool for Google Docs and Notion. It automates publishing to your favorite Content Management Systems.
- [Contentful graphql](https://composio.dev/toolkits/contentful_graphql) - Contentful graphql is a content delivery API that lets you access Contentful data using GraphQL queries. It gives you efficient, flexible ways to fetch and manage structured content for any digital project.
- [Conversion tools](https://composio.dev/toolkits/conversion_tools) - Conversion Tools is an online service for converting documents between formats such as PDF, Word, Excel, XML, and CSV. It lets you automate complex document workflows with just a few clicks.
- [Convertapi](https://composio.dev/toolkits/convertapi) - ConvertAPI is a robust file conversion service for documents, images, and spreadsheets. It streamlines programmatic format changes and lets developers automate complex workflows with a single API.
- [Craftmypdf](https://composio.dev/toolkits/craftmypdf) - CraftMyPDF is a web-based service for designing and generating PDFs with templates and live data. It streamlines document creation by automating personalized PDFs at scale.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Happy scribe MCP?

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

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

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

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