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+---
+title: Git Extension
+description: Add Git MCP Server as a Goose Extension
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import YouTubeShortEmbed from '@site/src/components/YouTubeShortEmbed';
+import CLIExtensionInstructions from '@site/src/components/CLIExtensionInstructions';
+import GooseDesktopInstaller from '@site/src/components/GooseDesktopInstaller';
+
+
+
+
+
+This tutorial covers how to add the [Git MCP Server](https://github.com/idosal/git-mcp) as a Goose extension to give LLMs live access to GitHub repos, enabling smart documentation search, code exploration, and accurate project insights.
+
+:::tip TLDR
+
+
+ [Launch the installer](goose://extension?cmd=npx&arg=mcp-remote&arg=https%3A%2F%2Fgitmcp.io%2Fdocs&id=gitmcp&name=GitMCP&description=Remote%20MCP%20server%20from%20gitmcp.io)
+
+
+ **Command**
+ ```sh
+ npx -y mcp-remote https://gitmcp.io/docs
+
+ ```
+
+
+:::
+
+## Configuration
+
+
+
+
+
+
+
+
+
+
+## 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
+
+
+
+ 1. Open a terminal and start a new Goose session:
+
+ ```sh
+ goose session
+ ```
+
+
+
+
+### 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
+
+:::note 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**
+- Supports 99+ languages
+- Some languages perform better than others
+- Check the [language performance breakdown](https://github.com/openai/whisper#available-models-and-languages) in the repository
+
+### 💾 **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.
+
+:::
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