Start with AI
EdgeOne Pages features built-in AI tools such as deployment MCP and AI IDE plugin, working in conjunction with flexible AI context files to build an intelligent free and open ecosystem, helping you deliver outstanding Web applications faster and with higher quality.
Using Pages MCP
MCP (Model Context Protocol) is an open protocol that enables AI models to securely interact with local and remote resources.
EdgeOne Pages Deploy MCP is a dedicated service that can quickly Deploy Web applications to EdgeOne Pages and generate public access links. This allows you to immediately preview and share AI-generated Web content. For more details, see Pages MCP.
AI Context File
In Pages, AI context files serve as a bridge for communication between you and AI IDEs such as CodeBuddy, Cursor, and Windsurf. These files are written in Markdown format, allowing you to define project-specific rules, best practices, code specifications, API definitions, and even business logic descriptions. With context files, you can provide precise context information to AI, ensuring the generated code, suggestions, and automated operations are more in line with your project requirements and platform features.
Using Mdc Files in AI IDEs
For most AI development tools, you only need to place the pages-llms.mdc file in the project root directory as a project-level rule or set it as a global rule in the development tool.
For use in CodeBuddy: Enter the settings interface in the IDE, locate the rule tab, and add the .mdc file under project rules. For more information, view the document Using CodeBuddy IDE.
For use in Cursor: Create a .cursor/rules/ folder in the project root directory and place your .mdc file there. Or add it to the user main directory (such as ~/.cursor/rules/). For more information, view the sample code in Cursor official documentation.
For use in Windsurf: Create a .windsurf/rules/ folder in the project root directory and place your .mdc file there. Or manually add this file through the Windsurf UI. For more information, view the sample code in Windsurf official documentation.
Using Llms.Txt
You can also use llms.txt in AI conversation to share the context of Pages documents. Typically, as long as the AI conversation tool supports this format, llms.txt will be referenced as a network resource context.
AI Development Assistance and Deployment Practice
The following best practice will guide you on how to collaborate with AI effectively for faster and better project development.
Optimize Prompt Content
Clear and structured prompts are key to effective communication with AI. Follow the rules below to help AI understand your intent more accurately.
Use the total score structure: First state the overall goal, then gradually refine specific requirements, such as "create a responsive blog homepage with navigation, article list and footer."
Use exact terminology: Use precise descriptions in key instructions, such as "implement a blue color scheme flat design with Tailwind CSS."
Provide existing examples: By providing existing code or design pattern examples, guide AI to generate content compliant with your style.
Continuously adjust and iterate: Try different expressions to organize prompt content for the best results caused by AI.
EdgeOne Pages Prompt Content Key Phrases
To help AI better understand your intent and generate project code for smooth deployment to Pages, mentioning these terms during AI dialogue can significantly improve the accuracy and relevance of AI output.
EdgeOne Pages: Clarify the specified target deployment platform.
Example: "Create a Next.js blog app for EdgeOne Pages."
Node Functions: Specify backend logic that requires the use of Node.js runtime environment.
Example: "I need a Node Functions to handle user authentication."
Edge Functions: Specify lightweight logic that requires edge computing capability.
Example: "Please write an Edge Functions to implement geolocation redirection."
Full-stack application: Emphasize the project includes frontend and backend.
Example: "Develop a full-stack application to get local weather in real time, using React for the frontend and Node Functions for the backend."
AI Rule file: Specify the use of a specific AI rule file to guide the AI.
Example: "Refer to the rules in the mdc file and generate code compliant with EdgeOne Pages standards."
Refine Project Information
Provide appropriate AI context to help the AI better understand your project.
Fully utilize context files: Supplement the project structure, API interface, or business logic appropriately in pages-llms.mdc. AI IDE will automatically load these rules to ensure the generated code meets project requirements.
Write a high-quality README: This is the key entry for AI to understand the project. A clear, detailed README.md should include project introduction, technology stack, deployment guide, and key module description to help AI quickly grasp the holistic picture and provide accurate recommendations.
