Build an MCP-powered Audio Analysis Toolkit

In an era of voice-driven interfaces and AI-powered assistants, extracting meaningful insights from audio is more important than ever. Whether you’re a developer, product builder, or AI tinkerer, this guide will show you how to build a full-fledged Audio Analysis Toolkit that can:

Transcribe audio with speaker labels and timestamps
Summarize long conversations
Detect sentiment and emotional tone
Extract key topics
Enable natural-language Q&A on audio content

The best part? You can use it either as:

A beautiful web UI powered by Streamlit, OR
A backend MCP server connected to Claude via Cursor for agentic interaction.

Let’s dive into both modes of using the toolkit.

Tech Stack Overview

AssemblyAI – API for audio transcription and advanced analysis (sentiment, topics, summarization)
Streamlit – Rapid frontend for building interactive UI
Cursor MCP (Model Context Protocol) – Interface for agents like Claude to call backend functions
Claude (via Cursor) – Agent that can consume your toolkit through natural language
Python 3.12+

Here’s our workflow:

User’s audio input is sent to AssemblyAI via a local MCP server.
AssemblyAI transcribes it while providing the summary, speaker labels, sentiment, and topics.
Post-transcription, the user can also chat with audio.

Dual Tool Structure: Transcription + Audio Analysis (via MCP)

Transcription MCP tool

This tool accepts an audio input from the user and transcribes it using AssemblyAI.

We also store the full transcript to use in the next tool.

Audio analysis tool

Next, we have a tool that returns specific insights from the transcript, like speaker labels, sentiment, topics, and summary.

Based on the user’s input query, the corresponding flags will be automatically set to True when the Agent will prepare the tool call via MCP:

Bonus: It’s RAG-Ready

Once transcribed, the transcript can even be indexed into a vector database or used in an LLM context window for true RAG-style audio Q&A, especially useful for:

Meeting assistants
Interview breakdowns
Legal depositions
Customer call analysis

Let’s implement this!

Option 1: Use as a Streamlit Web App

This mode is perfect if you want a user-friendly, visually rich interface for uploading and analyzing audio files.

This web-based interface is powered by Streamlit and comes with:

Audio file upload: .mp3, .wav, .mp4, etc.
Full transcript with timestamps
Speaker-wise breakdown
Sentiment visualized with emojis and colors
AI-generated summaries
Top topics and relevance
Ask questions about the audio in a chatbot-style interface

How to Run It?

1. Clone the repo and set up the environment.

				
					git clone https://github.com/edityeah/ai-hub.git
cd audio-analysis-toolkit
python -m venv .venv
source .venv/bin/activate    # Or .venv\Scripts\activate on Windows
pip install -r requirements.txt

2. Add your AssemblyAI API Key

Create a .env file:

				
					ASSEMBLYAI_API_KEY=your_key_here

Run the app

				
					streamlit run app.py

Option 2: Use as a Cursor MCP Server (for Claude Agents)

If you prefer working agentically — that is, using an AI like Claude to invoke capabilities — you can run this toolkit as an MCP server.

What is MCP?

MCP (Model Context Protocol) is how tools can plug into Cursor and be used by agents like Claude. It allows your local Python scripts to respond to natural language instructions inside the Cursor IDE.

Setting It Up:

Ensure server.py is in your project directory (this script exposes transcription logic via HTTP or CLI).
Configure Cursor MCP:
In Cursor → Preferences → MCP Servers → Global, add:

				
					{
  "mcpServers": {
    "assemblyai": {
      "command": "python",
      "args": ["server.py"],
      "env": {
        "ASSEMBLYAI_API_KEY": "your_key_here"
      }
    }
  }
}

3. Open the project folder in Cursor
Cursor will automatically launch the MCP server if the config and working directory are correct.

4. Call it from Claude:
Inside Cursor, invoke the tool like this:

				
					@assemblyai: transcribe "path/to/audio.mp3"

Claude will now interact with your audio analysis server in real-time and return intelligent answers.

Which One Should You Use?

Use Case	Streamlit UI	MCP + Claude
Non-technical users	✅ Best option	❌
Developer workflows	✅	✅
Chat-driven automation	⚠️ Basic	✅ Powerful
Visual dashboards	✅ Beautiful	❌
IDE integration	❌	✅ Native (Cursor)

Final Thoughts

With just a few lines of Python and AssemblyAI’s APIs, you can build a full-featured audio intelligence system. Whether you want to offer this as a product, internal tool, or agentic function inside your AI IDE, this toolkit gives you the flexibility to do both.

💡 Ready to build? Try both modes and see what fits your workflow best!

Also read

AI prototyping for product folks

How to Build a Local AI-Powered OCR App with Gemma-3 and Streamlit?

How I Built a Talking AI Agent That Sounds Like Me (in 100 Lines of Code)

Aditya Chaudhari

Product Manager

From wireframes to launchpads, Aditya engineers AI-powered experiences with the precision of a pilot and the soul of a designer.

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About the Author

I’m Aditya, a Mumbai-based design-centric Product Manager with a passion for all things bikes, airplanes, and dogs! With over 6 years of experience in the ever-evolving world of digital products, I’ve become the ultimate driving machine for crafting pixel-perfect experiences that’ll rev your engines. When I’m not obsessing over pixels, you can find me travelling. I thrive on collaborative projects and delight in finding innovative solutions that not only wow users but also drive tangible results. I take my work seriously, but I also believe in adding a sprinkle of humor and quirkiness to keep things exciting.

Aditya ChaudhariProduct Manager