Deepgram MCP Server

A Model Context Protocol (MCP) server that provides access to Deepgram's speech recognition and text-to-speech capabilities.

Features

• Audio Transcription: Convert audio to text with high accuracy

• Text-to-Speech: Generate natural-sounding speech from text with automatic compression

• Audio Analysis: Extract insights like sentiment, topics, intents, and entities

• Speaker Diarization: Identify different speakers in audio

• Language Detection: Automatically detect the language of audio

• Multiple Models: Support for various Deepgram models optimized for different use cases

• Smart Audio Compression: Automatically compresses generated audio files for efficient transfer

Installation

1. Clone this repository

2. Install dependencies:

   npm install

3. Copy the environment file and add your Deepgram API key:

   cp env.example .env # Edit .env and add your DEEPGRAM_API_KEY, OPENAI_API_KEY or GROQ_API_KEY (whatever you want to use)

4. Build the project:

   npm run build

Usage

HTTP Transport (Recommended for Production)

The server will start on port 8080 by default. You can specify a different port:

STDIO Transport (For Development)

Available Tools

1. transcribe_audio

Transcribe audio to text with various options for customization.

Parameters:

• audioUrl or audioData: Audio source (URL or base64)

• model: Deepgram model to use (default: "nova-2-general")

• language: Language code (default: "en")

• punctuate: Add punctuation (default: true)

• diarize: Speaker identification (default: false)

• sentiment: Sentiment analysis (default: false)

• And many more options...

2. text_to_speech

Convert text to speech using Deepgram's TTS models with automatic compression.

Parameters:

• text: Text to convert to speech (required)

• model: TTS model to use (default: "aura-asteria-en")

• voice: Voice selection

• format: Output format (default: "mp3")

• speed: Speech speed (default: 1.0)

Output:

• Original audio file saved to generated_audio/ folder

• Compressed audio data saved to compressed_audio/ folder

• Response includes file paths and compression metadata

3. analyze_audio

Perform advanced audio analysis including sentiment, topics, intents, and entities.

Parameters:

• audioUrl or audioData: Audio source

• features: Analysis features to enable

• model: Model for analysis

4. get_models

Get information about available Deepgram models.

Parameters:

• model_type: Filter by model type ("transcription", "tts", or "all")

Client Configuration

For MCP clients, use this configuration:

Development

API Key

Get your Deepgram API key from Deepgram Console.

Audio Compression System

The TTS functionality includes an intelligent compression system that:

• Automatically compresses generated audio files using gzip compression

• Saves compressed data to separate files to avoid large agent responses

• Provides decompression tools for easy audio file extraction

• Maintains quality while reducing file sizes by 2-4x

File Structure

Decompression Tools

Python Script (Recommended):

Node.js Script:

Agno Integration

This MCP server also includes integration with Agno, a high-performance runtime for multi-agent systems.

Agno Tests

The TTS test will:

1. Generate audio with automatic compression

2. Save the response to tts_response.json

3. Decompress the audio file to generated_audio/

License

MIT

Developer

• Dheeraj Mudireddy (meetdheerajreddy@gmail.com)