The Spec-driven Development MCP Server provides an AI-guided, structured workflow for complete software development from concept to execution.
Core Workflow Capabilities:
- Start & Define: Initiate development workflows (
spec_driven_dev_workflow_start
) and confirm feature goals with specific names and summaries - Requirements Gathering: Collect and confirm detailed requirements in EARS format using
spec_driven_dev_requirements_start
andspec_driven_dev_requirements_confirmed
- Technical Design: Create and confirm comprehensive design documentation covering architecture, components, and data models
- Task Planning: Break down features into executable, manageable tasks with confirmation workflows
- Execution: Systematically guide task implementation with
spec_driven_dev_execute_start
, optionally targeting specific tasks
Documentation & Organization:
- Generates structured outputs (
requirements.md
,design.md
,tasks.md
) in feature-specific directories - Creates organized project documentation throughout the development lifecycle
Integration:
- Compatible with AI-powered IDEs like Cursor and Claude Desktop
- Supports JSON configuration for seamless setup and interactive development experience
Mentioned in the context of OAuth login options for the user authentication feature example
Provides OAuth authentication integration as demonstrated in the example authentication workflow
Serves as the runtime environment for the MCP server, with version 18+ required for development
Provides package installation and management for the MCP server
Recommended as the preferred package manager for development of the MCP server
Spec-driven Development MCP Server
An MCP server that brings AI-guided spec-driven development workflow to any AI-powered IDEs beyonnd Kiro. Transform your development process with structured, step-by-step guidance from idea to implementation.
What is Spec-driven Development?
Spec-driven development is a methodology that emphasizes creating detailed specifications before writing code. This approach helps ensure clear requirements, better design decisions, and more maintainable code. Our MCP server guides you through this process with AI assistance.
Features
- Complete Development Workflow: From goal collection to task execution
- AI-Powered Guidance: Step-by-step instructions for each development phase
- Structured Documentation: Generates organized specs in EARS format
- Template-Based: Uses proven templates for requirements, design, and tasks
Installation
Installing via Smithery
To install spec-driven-dev-mcp for Claude Desktop automatically via Smithery:
Using npx (Recommended)
Using npm
Usage
With Cursor
Add to your Cursor MCP settings:
Available Tools
- spec_driven_dev_workflow_start - Start the development workflow
- spec_driven_dev_goal_confirmed - Confirm feature goals
- spec_driven_dev_requirements_start - Begin requirements gathering
- spec_driven_dev_requirements_confirmed - Confirm requirements completion
- spec_driven_dev_design_start - Start design documentation
- spec_driven_dev_design_confirmed - Confirm design completion
- spec_driven_dev_tasks_start - Begin task planning
- spec_driven_dev_tasks_confirmed - Confirm task planning completion
- spec_driven_dev_execute_start - Start task execution
Workflow Stages & Example
The spec-driven development process follows five distinct stages. Here's how it works with a real example - building a user authentication feature:
1. Goal Collection - Define What You Want to Build
Purpose: Establish clear, specific objectives for your feature.
Example Interaction:
2. Requirements Gathering - Create Detailed EARS-format Requirements
Purpose: Transform your goals into specific, testable requirements using the EARS (Easy Approach to Requirements Syntax) format.
Example Interaction:
3. Design Documentation - Technical Architecture and Design
Purpose: Create detailed technical specifications including architecture, component design, data models, and API specifications.
Example Interaction:
4. Task Planning - Break Down into Executable Tasks
Purpose: Decompose the feature into specific, prioritized development tasks with clear dependencies.
Example Interaction:
5. Task Execution - Implement the Code
Purpose: Execute the planned tasks systematically, implementing the feature according to specifications.
Example Interaction:
Generated Project Structure
Throughout the workflow, the following documentation structure is created:
Development
Prerequisites
Make sure you have Node.js 18+ installed.
Setup
Running the Project
Contributing & License
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
License
MIT
Attribution
This project was inspired by and builds upon concepts from vibedevtools by @yinwm, a collection of development efficiency tools.
remote-capable server
The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.
Tools
An MCP server that enables AI-powered IDEs to implement a structured development workflow from requirements gathering to code implementation, guiding users through goal collection, requirements specification, design documentation, task planning, and execution.
Related MCP Servers
- AsecurityFlicenseAqualityAn MCP server that supercharges AI assistants with powerful tools for software development, enabling research, planning, code generation, and project scaffolding through natural language interaction.Last updated -1150268TypeScript
- AsecurityAlicenseAqualityA MCP server that enables human-in-the-loop workflow in AI-assisted development tools by allowing users to run commands, view their output, and provide textual feedback directly to the AI assistant.Last updated -11,484PythonMIT License
- AsecurityAlicenseAqualityA powerful MCP server that provides interactive user feedback and command execution capabilities for AI-assisted development, featuring a graphical interface with text and image support.Last updated -135PythonMIT License
- AsecurityFlicenseAqualityMCP server that enables human-in-the-loop workflow in AI-assisted development tools by allowing users to provide direct feedback to AI agents without consuming additional premium requests.Last updated -11Python