Building a REST API with Claude Code: A Planning Session Walkthrough
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Building a REST API with Claude Code: A Planning Session Walkthrough

Backend Reporter
3 min read

A detailed look at the planning and implementation process for building a REST API using Claude Code, showing the step-by-step approach to creating a task management service with Node.js, Express, and SQLite.

This article documents a real Claude Code session where we planned and built a REST API for task management. The session demonstrates how AI-assisted development can streamline the planning and implementation process, providing transparency into the decision-making behind the code.

The Planning Phase

The session began with a clear objective: build a REST API for task management using Node.js, Express, and SQLite. The requirements were specific - include CRUD endpoints, validation, and a health check. This clarity is crucial for any development project, as it sets the foundation for all subsequent decisions.

Architecture Decisions

The chosen stack reflects a pragmatic approach:

  • Node.js for the runtime environment
  • Express for the web framework (lightweight and unopinionated)
  • SQLite for the database (perfect for development and small-scale deployments)
  • Zod for request validation (TypeScript-first schema validation)

This combination offers a good balance between simplicity and functionality, making it ideal for a demo or internal service.

Implementation Strategy

The implementation followed a logical progression:

  1. Project initialization - Setting up the basic project structure
  2. Dependency installation - Installing Express, SQLite3, Zod, and nodemon for development
  3. Database bootstrap logic - Creating the initial database schema
  4. CRUD route implementation - Building the core API endpoints
  5. Validation and error handling - Ensuring data integrity and proper error responses
  6. Verification - Testing the API with sample requests

The Build Process

After initializing the project and installing dependencies, the next step was creating the core API implementation. The single-file approach (server.js) keeps things simple for this demonstration, though in production you'd likely separate concerns into different modules.

The API includes these endpoints:

  • GET /health - Health check endpoint
  • GET /tasks - Retrieve all tasks
  • GET /tasks/:id - Retrieve a specific task
  • POST /tasks - Create a new task
  • PUT /tasks/:id - Update an existing task
  • PATCH /tasks/:id/status - Update task status
  • DELETE /tasks/:id - Delete a task

Validation and Error Handling

Using Zod for validation ensures that incoming requests meet the expected schema before processing. This is a critical aspect of building robust APIs, as it prevents malformed data from entering your system and provides clear error messages to API consumers.

Testing the API

The session concluded with a smoke test using curl commands to verify the API works as expected. This immediate feedback loop is valuable for catching issues early in the development process.

Production Considerations

The architecture summary notes that while this is a solid baseline for demos or internal services, production deployments would need additional considerations:

  • Authentication - Securing the API endpoints
  • Structured logging - Better observability and debugging
  • Tests - Ensuring reliability through automated testing
  • Database migrations - Managing schema changes over time

Why This Matters

Publishing the planning session provides transparency into the development process. It's not just about the final code - understanding the reasoning behind architectural decisions, the trade-offs considered, and the step-by-step implementation helps readers learn the "why" behind the "what."

This approach to documenting development work is particularly valuable in the era of AI-assisted coding, where tools like Claude Code can accelerate development but still require human oversight and decision-making. By sharing the planning and build process, developers can learn from each other's approaches and improve their own workflows.

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The session demonstrates that even with AI assistance, thoughtful planning and systematic implementation remain essential for building quality software. The transparency in sharing the planning phase helps demystify the development process and provides a template for others to follow or adapt for their own projects.

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