GitMCP Extension
Watch the demo
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.
- Goose Desktop
- Goose CLI
Command
npx -y mcp-remote https://gitmcp.io/docs
Configurationβ
- Goose Desktop
- Goose CLI
- Launch the installer
- Click
OK
to confirm the installation - Click the button in the top-left to open the sidebar
- Navigate to the chat
- Run the
configure
command:
goose configure
- Choose to add a
Command-line Extension
.
β goose-configure
β
β What would you like to configure?
β Add Extension
β
β What type of extension would you like to add?
β β Built-in Extension
β β Command-line Extension (Run a local command or script)
β β Remote Extension (SSE)
β β Remote Extension (Streaming HTTP)
β
- Give your extension a name.
β goose-configure
β
β What would you like to configure?
β Add Extension
β
β What type of extension would you like to add?
β Command-line Extension
β
β What would you like to call this extension?
β Git MCP
β
- Enter the command to run when this extension is used.
β goose-configure
β
β What would you like to configure?
β Add Extension
β
β What type of extension would you like to add?
β Command-line Extension
β
β What would you like to call this extension?
β Git MCP
β
β What command should be run?
β npx -y mcp-remote https://gitmcp.io/docs
β
- Enter the number of seconds Goose should wait for actions to complete before timing out. Default is
300
seconds.
β goose-configure
β
β What would you like to configure?
β Add Extension
β
β What type of extension would you like to add?
β Command-line Extension
β
β What would you like to call this extension?
β Git MCP
β
β What command should be run?
β npx -y mcp-remote https://gitmcp.io/docs
β
β Please set the timeout for this tool (in secs):
β 300
β
- Choose to add a description. If you select
No
, Goose will skip it.
β goose-configure
β
β What would you like to configure?
β Add Extension
β
β What type of extension would you like to add?
β Command-line Extension
β
β What would you like to call this extension?
β Git MCP
β
β What command should be run?
β npx -y mcp-remote https://gitmcp.io/docs
β
β Please set the timeout for this tool (in secs):
β 300
β
β Would you like to add a description?
β No
β
- Choose
No
when asked to add environment variables.
β goose-configure
β
β What would you like to configure?
β Add Extension
β
β What type of extension would you like to add?
β Command-line Extension
β
β What would you like to call this extension?
β Git MCP
β
β What command should be run?
β npx -y mcp-remote https://gitmcp.io/docs
β
β Please set the timeout for this tool (in secs):
β 300
β
β Would you like to add a description?
β No
β
β Would you like to add environment variables?
β No
β
β Added Git MCP extension
Example Usageβ
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 usageβall directly from the source.
- Goose Desktop
- Goose CLI
- Open a new session in Goose Desktop
- Open a terminal and start a new Goose session:
goose session
Goose Promptβ
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 Outputβ
I'll help you understand OpenAI Whisper by exploring their GitHub repository. Let me fetch the documentation and relevant information.
βββ owner | repo ββββββββββββββββββββββββββ
Now let me search for some specific setup and usage information:
βββ owner | query ββββββββ repo ββββββββββββ
Let me search for more technical details and examples:
βββ owner | query ββββββββ repo ββββββββββββ
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:
- β Robust Performance: Trained on 680,000 hours of diverse, multilingual audio data
- β Zero-shot Transfer: Works on many languages without fine-tuning
- β Multiple Model Sizes: From tiny (39M params) to large (1.55B params)
- β Easy to Use: Simple command-line interface and Python API
- β 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 Checklistβ
- β Install Python 3.8-3.11
- β Install FFmpeg for your operating system
- β
Install Whisper:
pip install -U openai-whisper
- β
Test installation:
whisper --help
- β
Try basic transcription:
whisper your_audio_file.mp3
First Runβ
On your first run, Whisper will automatically download the model weights:
tiny
: ~39 MB downloadbase
: ~142 MB downloadsmall
: ~461 MB downloadmedium
: ~1.42 GB downloadlarge
: ~2.87 GB downloadturbo
: ~1.5 GB download
Models are cached locally, so subsequent runs are much faster.
Tips & Best Practicesβ
π― Model Selectionβ
- English-only: Use
.en
models (base.en
,small.en
, etc.) - Fast transcription: Use
tiny
orbase
models - High accuracy: Use
medium
orlarge
models - Translation tasks: Use
medium
orlarge
(NOTturbo
)
π΅ Audio Qualityβ
- Clean, clear audio produces better results
- Whisper handles various audio formats (MP3, WAV, FLAC, M4A, etc.)
- Background noise may affect accuracy
π Language Supportβ
- Supports 99+ languages
- Some languages perform better than others
- Check the language performance breakdown in the repository
πΎ Resource Managementβ
- 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.