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How to integrate Happy scribe MCP with Autogen

Connect Autogen to Happy scribe MCP. Transcribe this podcast episode to text, generate subtitles for uploaded video file, and more using natural language, with authentication handled for you.

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Happy Scribe is an automatic transcription service that converts audio and video files into accurate text. It lets you quickly generate transcripts and subtitles, saving you hours of manual work.

19 Tools

Introduction

This guide walks you through connecting Happy scribe to AutoGen using the Composio tool router. By the end, you'll have a working Happy scribe agent that can transcribe audio and video, create subtitles, order translations, export transcripts, and list your transcriptions through natural language commands.

This guide will help you understand how to give your AutoGen 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

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TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Happy scribe
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Happy scribe tools
  • Run a live chat loop where you ask the agent to perform Happy scribe operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

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.

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

What is Composio SDK?

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

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

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

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

Step-by-step Guide

Step by step08 STEPS
1

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Happy scribe account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Happy scribe via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Happy scribe connections to use
5

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Happy scribe session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["happy_scribe"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Happy scribe tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed
7

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Happy scribe assistant agent with MCP tools
    agent = AssistantAgent(
        name="happy_scribe_assistant",
        description="An AI assistant that helps with Happy scribe operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Happy scribe tools from the workbench
8

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Happy scribe related question or task to the agent.\n")

# Conversation loop
while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Happy scribe tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Happy scribe and AutoGen:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Happy scribe session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["happy_scribe"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Happy scribe assistant agent with MCP tools
        agent = AssistantAgent(
            name="happy_scribe_assistant",
            description="An AI assistant that helps with Happy scribe operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
        print("Ask any Happy scribe related question or task to the agent.\n")

        # Conversation loop
        while True:
            user_input = input("You: ").strip()

            if user_input.lower() in ['exit', 'quit', 'bye']:
                print("\nGoodbye!")
                break

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You now have an Autogen assistant wired into Happy scribe through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Happy scribe, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
TOOLS

Supported Tools

Every Happy scribe action and event your agent gets out of the box.

Create Subtitle

Creates a transcription job from a publicly accessible media URL, configured for subtitles by default.

Create Translation Task

Creates an AI-powered translation task to translate an existing transcription into another language.

Delete Transcription

Tool to delete a transcription job.

Delete Webhook

Deletes a webhook by its ID.

Get Account Details

Requests Happy Scribe account information, optionally scoped to a specified organization.

Get Supported Languages

Returns a static reference list of language codes and recorded human transcription service availability.

Get API Rate Limit

Returns the documented default transcription-creation rate limit of 200 requests per hour.

Get Signed Upload URL

Tool to get a signed URL for uploading a file to Happy Scribe's S3 storage.

Confirm Order

Tool to confirm a pending order.

Create Translation Order

Tool to create a translation order from an existing transcription.

Export Transcription

Creates an export job for transcription content in supported document, subtitle, video-editing, and other formats.

Get API Version

Identifies the Happy Scribe API version associated with the connection.

Get Error Codes

Returns a static reference list of selected Happy Scribe HTTP error codes and their meanings.

Get Supported Formats

Returns input and output file format lists to help plan transcription or subtitle work.

Get Transcription Details

Tool to retrieve details and status of a specific transcription job.

Get Webhooks

Tool to retrieve webhooks configured for your account.

Retrieve Export

Tool to retrieve information about a specific export.

List Transcriptions

Retrieves a paginated list of transcription jobs for a Happy Scribe organization.

Retrieve Order

Retrieve details of a Happy Scribe order by its ID.

FRAMEWORKS

How to build Happy scribe MCP Agent with another framework

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Claude Code

Use Happy scribe MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Happy scribe MCP with Claude Cowork

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Happy scribe MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Happy scribe MCP with Claude Agent SDK

Codex logo

Codex

Use Happy scribe MCP with Codex

OpenClaw logo

OpenClaw

Use Happy scribe MCP with OpenClaw

Hermes logo

Hermes

Use Happy scribe MCP with Hermes

Atomic Agent logo

Atomic Agent

Use Happy scribe MCP with Atomic Agent

Google ADK logo

Google ADK

Use Happy scribe MCP with Google ADK

LangChain logo

LangChain

Use Happy scribe MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Happy scribe MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Happy scribe MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Happy scribe MCP with LlamaIndex

CrewAI logo

CrewAI

Use Happy scribe MCP with CrewAI

Pydantic AI logo

Pydantic AI

Use Happy scribe MCP with Pydantic AI

FAQ

Frequently asked questions

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.

Yes, you can. Autogen 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.

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.

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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