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GitMCP Extension

This tutorial covers how to add the Git MCP Server as a Goose extension to give LLMs live access to GitHub repos, enabling smart documentation search, code exploration, and accurate project insights.

TLDR

Launch the installer

Configuration​

  1. Launch the installer
  2. Click OK to confirm the installation
  3. Click the button in the top-left to open the sidebar
  4. Navigate to the chat

Example Usage​

In this example, Goose uses GitMCP to pull real-time documentation from the openai/whisper GitHub repository, explore how the speech-to-text model works, and surface accurate setup instructions and command-line usage—all directly from the source.

  1. Open a new session in Goose Desktop

Goose Prompt​

  Can you help me understand how the OpenAI Whisper speech-to-text model works?

  Use the GitHub repository `openai/whisper` to find accurate information.

  Please:
  - Summarize what the model does and its key features
  - Show an example of how to run it from the command line
  - Explain what files or models are needed to get started
  - Include any useful code snippets or setup instructions you find in the repo

  Give me the response in a Markdown guide format.

Goose Output​

Desktop

I'll help you understand OpenAI Whisper by exploring their GitHub repository. Let me fetch the documentation and relevant information.

─── owner | repo ──────────────────────────

Now let me search for some specific setup and usage information:

─── owner | query ──────── repo ────────────

Let me search for more technical details and examples:

─── owner | query ──────── repo ────────────

Perfect! Now I have all the information I need from the OpenAI Whisper repository. Let me create a comprehensive Markdown guide for you.

OpenAI Whisper Speech-to-Text Model Guide

Overview Whisper is a general-purpose speech recognition model developed by OpenAI. It's a powerful, multitasking neural network that can perform:

  • Multilingual speech recognition (99+ languages)
  • Speech translation (convert foreign speech to English text)
  • Language identification (detect what language is being spoken)
  • Voice activity detection (identify when speech is present)

Key Features:

  • ✅ Robust Performance: Trained on 680,000 hours of diverse, multilingual audio data
  • ✅ Zero-shot Transfer: Works on many languages without fine-tuning
  • ✅ Multiple Model Sizes: From tiny (39M params) to large (1.55B params)
  • ✅ Easy to Use: Simple command-line interface and Python API
  • ✅ Open Source: MIT licensed with freely available model weights

Architecture Whisper uses a Transformer sequence-to-sequence model that processes audio in 30-second chunks. It converts audio to log-Mel spectrograms and uses special tokens to specify tasks (transcribe, translate, language ID, etc.).

Command-Line Usage Basic Transcription

Getting Started Checklist​

  1. ✅ Install Python 3.8-3.11
  2. ✅ Install FFmpeg for your operating system
  3. ✅ Install Whisper: pip install -U openai-whisper
  4. ✅ Test installation: whisper --help
  5. ✅ Try basic transcription: whisper your_audio_file.mp3

First Run​

On your first run, Whisper will automatically download the model weights:

  • tiny: ~39 MB download
  • base: ~142 MB download
  • small: ~461 MB download
  • medium: ~1.42 GB download
  • large: ~2.87 GB download
  • turbo: ~1.5 GB download

Models are cached locally, so subsequent runs are much faster.

Tips & Best Practices​

🎯 Model Selection​

  • English-only: Use .en models (base.en, small.en, etc.)
  • Fast transcription: Use tiny or base models
  • High accuracy: Use medium or large models
  • Translation tasks: Use medium or large (NOT turbo)

🎵 Audio Quality​

  • Clean, clear audio produces better results
  • Whisper handles various audio formats (MP3, WAV, FLAC, M4A, etc.)
  • Background noise may affect accuracy

🌍 Language Support​

💾 Resource Management​

  • Larger models require more VRAM/RAM
  • Consider your hardware limitations when choosing models
  • Use smaller models for real-time applications

Whisper is released under the MIT License, making it free to use for both personal and commercial projects.