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docs: GitMCP Tutorial (#3708)
Signed-off-by: Ebony Louis <ebony774@gmail.com>
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Ebony Louis
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---
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title: Git Extension
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description: Add Git MCP Server as a Goose Extension
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---
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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import YouTubeShortEmbed from '@site/src/components/YouTubeShortEmbed';
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import CLIExtensionInstructions from '@site/src/components/CLIExtensionInstructions';
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import GooseDesktopInstaller from '@site/src/components/GooseDesktopInstaller';
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<!-- <YouTubeShortEmbed videoUrl="https://www.youtube.com/embed/LNmPi6YCocI" /> -->
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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.
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:::tip TLDR
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<Tabs groupId="interface">
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<TabItem value="ui" label="Goose Desktop" default>
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[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)
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</TabItem>
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<TabItem value="cli" label="Goose CLI">
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**Command**
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```sh
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npx -y mcp-remote https://gitmcp.io/docs
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```
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</TabItem>
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</Tabs>
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:::
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## Configuration
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<Tabs groupId="interface">
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<TabItem value="ui" label="Goose Desktop" default>
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<GooseDesktopInstaller
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extensionId="Git-mcp"
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extensionName="Git mcp"
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extensionDescription="Git MCP server for up-to-date docs and project insights"
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command="npx"
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args={["-y", "mcp-remote" , "https://gitmcp.io/docs"]}
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cliCommand="npx -y mcp-remote https://gitmcp.io/docs"
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timeout={300}
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note="Note that you'll need Node.js installed on your system to run this command, as it uses npx."
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/>
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</TabItem>
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<TabItem value="cli" label="Goose CLI">
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<CLIExtensionInstructions
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name="Git MCP"
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command="npx -y mcp-remote https://gitmcp.io/docs"
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timeout={300}
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/>
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</TabItem>
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</Tabs>
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## Example Usage
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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.
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<Tabs groupId="interface">
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<TabItem value="ui" label="Goose Desktop" default>
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1. Open a new session in Goose Desktop
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</TabItem>
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<TabItem value="cli" label="Goose CLI">
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1. Open a terminal and start a new Goose session:
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```sh
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goose session
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```
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</TabItem>
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</Tabs>
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### Goose Prompt
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Can you help me understand how the OpenAI Whisper speech-to-text model works?
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Use the GitHub repository `openai/whisper` to find accurate information.
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Please:
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- Summarize what the model does and its key features
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- Show an example of how to run it from the command line
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- Explain what files or models are needed to get started
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- Include any useful code snippets or setup instructions you find in the repo
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Give me the response in a Markdown guide format.
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### Goose Output
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:::note Desktop
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I'll help you understand OpenAI Whisper by exploring their GitHub repository. Let me fetch the documentation and relevant information.
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`─── owner | repo ──────────────────────────`
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Now let me search for some specific setup and usage information:
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`─── owner | query ──────── repo ────────────`
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Let me search for more technical details and examples:
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`─── owner | query ──────── repo ────────────`
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Perfect! Now I have all the information I need from the OpenAI Whisper repository. Let me create a comprehensive Markdown guide for you.
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OpenAI Whisper Speech-to-Text Model Guide
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Overview
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Whisper is a general-purpose speech recognition model developed by OpenAI. It's a powerful, multitasking neural network that can perform:
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- Multilingual speech recognition (99+ languages)
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- Speech translation (convert foreign speech to English text)
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- Language identification (detect what language is being spoken)
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- Voice activity detection (identify when speech is present)
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Key Features:
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- ✅ Robust Performance: Trained on 680,000 hours of diverse, multilingual audio data
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- ✅ Zero-shot Transfer: Works on many languages without fine-tuning
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- ✅ Multiple Model Sizes: From tiny (39M params) to large (1.55B params)
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- ✅ Easy to Use: Simple command-line interface and Python API
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- ✅ Open Source: MIT licensed with freely available model weights
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Architecture
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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.).
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Command-Line Usage
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Basic Transcription
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## Getting Started Checklist
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1. **✅ Install Python 3.8-3.11**
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2. **✅ Install FFmpeg** for your operating system
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3. **✅ Install Whisper**: `pip install -U openai-whisper`
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4. **✅ Test installation**: `whisper --help`
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5. **✅ Try basic transcription**: `whisper your_audio_file.mp3`
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### First Run
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On your first run, Whisper will automatically download the model weights:
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- `tiny`: ~39 MB download
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- `base`: ~142 MB download
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- `small`: ~461 MB download
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- `medium`: ~1.42 GB download
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- `large`: ~2.87 GB download
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- `turbo`: ~1.5 GB download
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Models are cached locally, so subsequent runs are much faster.
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## Tips & Best Practices
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### 🎯 **Model Selection**
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- **English-only**: Use `.en` models (`base.en`, `small.en`, etc.)
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- **Fast transcription**: Use `tiny` or `base` models
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- **High accuracy**: Use `medium` or `large` models
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- **Translation tasks**: Use `medium` or `large` (NOT `turbo`)
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### 🎵 **Audio Quality**
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- Clean, clear audio produces better results
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- Whisper handles various audio formats (MP3, WAV, FLAC, M4A, etc.)
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- Background noise may affect accuracy
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### 🌍 **Language Support**
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- Supports 99+ languages
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- Some languages perform better than others
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- Check the [language performance breakdown](https://github.com/openai/whisper#available-models-and-languages) in the repository
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### 💾 **Resource Management**
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- Larger models require more VRAM/RAM
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- Consider your hardware limitations when choosing models
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- Use smaller models for real-time applications
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Whisper is released under the **MIT License**, making it free to use for both personal and commercial projects.
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:::
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