MCP Servers

groq-whisper-mcp

Transcribe audio/video files using Groq-hosted Whisper with word-level timestamps, automatic caching, and cost estimation, offering up to 9x savings over OpenAI Whisper.

README

groq-whisper-mcp

MCP server for audio/video transcription via Groq-hosted Whisper. Word-level timestamps, automatic caching, cost estimation.

9x cheaper than OpenAI Whisper — same model, different API.

Provider	Model	Cost/hour
Groq	whisper-large-v3-turbo	$0.04
Groq	whisper-large-v3	$0.111
Groq	distil-whisper	$0.02
OpenAI	whisper-1	$0.36

Prerequisites

Python 3.10+
ffmpeg (for audio extraction from video)
Groq API key

Installation

git clone https://github.com/bis-code/groq-whisper-mcp.git
cd groq-whisper-mcp
python3 -m venv venv
venv/bin/pip install mcp openai

Claude Code Configuration

Add to your project's .mcp.json:

{
  "mcpServers": {
    "whisper": {
      "command": "/path/to/groq-whisper-mcp/venv/bin/python",
      "args": ["-m", "server"],
      "cwd": "/path/to/groq-whisper-mcp/src",
      "env": {
        "GROQ_API_KEY": "your-groq-api-key"
      }
    }
  }
}

Tools

`transcribe_video`

Transcribe a video file with word-level timestamps.

Parameter	Type	Required	Description
`video_path`	string	yes	Path to video file
`model`	string	no	Whisper model (default: `whisper-large-v3-turbo`)
`force_retranscribe`	boolean	no	Bypass cache (default: `false`)

Returns full text, word-level timestamps [{word, start, end}], and duration. Results are cached per-project.

`estimate_transcription_cost`

Estimate cost before transcribing.

Parameter	Type	Required	Description
`video_path`	string	yes	Path to video file
`model`	string	no	Whisper model (default: `whisper-large-v3-turbo`)

Returns duration, estimated cost, and comparison with OpenAI pricing.

How It Works

Extracts audio from video via ffmpeg (128kbps MP3, falls back to 64kbps if >25MB)
Sends to Groq's OpenAI-compatible API (same openai SDK, different base_url)
Returns word-level timestamps with ~0.1s precision
Caches results as whisper_words.json alongside the video

Running Tests

PYTHONPATH=src venv/bin/pytest tests/ -v

License

MIT

Recommended Servers

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official

Featured

TypeScript

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official

Featured

Local

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official

Featured

TypeScript

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official

Featured

Python

E2B

Using MCP to run code via e2b.

Official

Featured

Neon Database

MCP server for interacting with Neon Management API and databases

Official

Featured

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official

Featured

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official

Featured