From bbaab5a5ec0ac0db2ff8109bd031d4bac1d85531 Mon Sep 17 00:00:00 2001 From: Ebony Louis Date: Tue, 29 Jul 2025 11:25:08 -0400 Subject: [PATCH] gitmcp tutorial Signed-off-by: Ebony Louis --- documentation/docs/mcp/git-mcp.md | 178 ++++++++++++++++++++++++++++++ 1 file changed, 178 insertions(+) create mode 100644 documentation/docs/mcp/git-mcp.md diff --git a/documentation/docs/mcp/git-mcp.md b/documentation/docs/mcp/git-mcp.md new file mode 100644 index 000000000000..01046056570b --- /dev/null +++ b/documentation/docs/mcp/git-mcp.md @@ -0,0 +1,178 @@ +--- +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. + +::: \ No newline at end of file