About Layer Prompt Manager
Layer Prompt Manager is an MCP server and web interface for saving and organizing AI prompts tied to code repositories. It is intended for developers and teams using AI-powered IDEs, with prompt version history and reusable templates.
Key features
- 01Save prompts from an IDE
- 02Edit and organize prompts
- 03Track, compare, and restore versions
- 04Create and reuse prompt templates
Use cases
- 01Build a prompt library for a codebase
- 02Review changes to prompts over time
- 03Standardize prompt patterns across a team
MCP support
The MCP server connects an IDE to Layer, enabling prompts to be created and saved from the development environment.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMELayer Prompt Manager

Save, version, and manage custom AI prompts for your code repositories. Seamlessly integrate with AI-powered IDEs like Cursor and GitHub Copilot.
Features
1. Save Prompts from Your IDE
Create and save custom prompts directly from your AI-powered IDE. The MCP server connects your development environment to Layer, allowing you to build a library of effective prompts specific to your codebase.
2. Version Control for Prompts
Track the evolution of your prompts over time. Compare different versions to see which ones produce the best results. Easily roll back to previous versions when needed.
3. Layer Prompts
- Access all your Layer prompts
- Edit existing prompts
- Create new prompts for AI-powered coding assistants
- Manage prompt versions
4. Templates
- Use pre-existing templates
- Create your own templates
- Standardize AI interactions across your development team
- Share and reuse common prompt patterns
5. Modern UI
- Matrix-inspired design
- Dark mode interface
- Responsive layout
- Interactive components
Project Structure
├── frontend/
│ ├── app/
│ │ ├── prompts/ # Prompt management pages
│ │ ├── templates/ # Template management pages
│ │ ├── tools/ # Tool-related components
│ │ └── docs/ # Documentation pages
│ ├── components/ # Reusable UI components
│ └── lib/ # API and utility functions
├── backend/
│ └── main.py # FastAPI backend server
Setup Instructions
Prerequisites
- Node.js (v16 or higher)
- Python 3.8+
- pip
- SQLite
Backend Setup
- Create a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
- Install dependencies:
pip install -r requirements.txt
- Create a
.envfile in the root directory:
LAYER_API_KEY=your_layer_api_key
LAYER_BASE_URL=https://api.buildwithlayer.com
- Start the backend server:
uvicorn main:app --reload
Frontend Setup
- Install dependencies:
cd frontend
npm install
- Create a
.env.localfile:
NEXT_PUBLIC_API_URL=http://localhost:8000
- Start the development server:
npm run dev
Usage
-
Creating Prompts
- Navigate to "Layer Prompts"
- Click "Create New Prompt"
- Fill in prompt details, steps, and arguments
- Save and version your prompt
-
Managing Templates
- Go to "Templates"
- Create new templates or use existing ones
- Edit and customize templates for your team
-
Version Control
- Each prompt can have multiple versions
- Add change notes for version tracking
- Compare and restore previous versions
Environment Variables
Backend (.env):
LAYER_API_KEY=your_layer_api_key
LAYER_BASE_URL=https://api.buildwithlayer.com
Frontend (.env.local):
NEXT_PUBLIC_API_URL=http://localhost:8000
.gitignore
# Dependencies
node_modules/
venv/
__pycache__/
# Environment variables
.env
.env.local
.env.development.local
.env.test.local
.env.production.local
# Build outputs
.next/
build/
dist/
# IDE
.vscode/
.idea/
# Database
*.db
*.sqlite3
# Logs
*.log
npm-debug.log*
yarn-debug.log*
yarn-error.log*
# System Files
.DS_Store
Thumbs.db
Contributing
- Fork the repository
- Create a feature branch
- Commit your changes
- Push to the branch
- Create a Pull Request
License
MIT License - feel free to use this project for your own purposes.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.