Spec-driven Development MCP Server

Spec-driven Development MCP Server

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.

Category
Visit Server

README

Spec-driven Development MCP Server

An MCP server that brings AI spec-driven development workflow to any AI-powered IDE besides Kiro

Features

  • Complete Development Workflow: From goal collection to task execution
  • AI-Powered Guidance: Step-by-step instructions for each development phase
  • Template-Based: Uses proven templates for requirements, design, and tasks
  • IDE Integration: Seamlessly integrates with Cursor, Copilot or any AI-powered IDE

Installation

Using npx (Recommended)

# Always get the latest version
npx spec-driven-dev-mcp@latest

# Or simply (will also get latest)
npx spec-driven-dev-mcp

Using npm

npm install -g spec-driven-dev-mcp
spec-driven-dev-mcp

Usage

With Cursor

Add to your Cursor MCP settings:

{
  "mcpServers": {
    "spec-driven-dev-mcp": {
      "command": "npx",
      "args": ["spec-driven-dev-mcp@latest"],
      "env": {},
      "disabled": false
    }
  }
}

Available Tools

  1. spec_driven_dev_workflow_start - Start the development workflow
  2. spec_driven_dev_goal_confirmed - Confirm feature goals
  3. spec_driven_dev_requirements_start - Begin requirements gathering
  4. spec_driven_dev_requirements_confirmed - Confirm requirements completion
  5. spec_driven_dev_design_start - Start design documentation
  6. spec_driven_dev_design_confirmed - Confirm design completion
  7. spec_driven_dev_tasks_start - Begin task planning
  8. spec_driven_dev_tasks_confirmed - Confirm task planning completion
  9. spec_driven_dev_execute_start - Start task execution

Workflow Stages

  1. Goal Collection - Define what you want to build
  2. Requirements Gathering - Create detailed EARS-format requirements
  3. Design Documentation - Technical architecture and design
  4. Task Planning - Break down into executable tasks
  5. Task Execution - Implement the code

Project Structure

Generated projects follow this structure:

.docs/specs/{feature_name}/
├── requirements.md
├── design.md
└── tasks.md

Example Usage

Here's a complete example of using the VibeSpecs workflow:

1. Start the Workflow

User: I want to build a user authentication feature
Assistant: Let me start the VibeSpecs workflow for you.
[calls spec_driven_dev_workflow_start]

2. Goal Collection

Assistant: Welcome to VibeSpecs! Let's define your feature goals:

1. What specific authentication methods do you want to support?
2. Do you need social login (Google, GitHub, etc.)?
3. What user roles and permissions are required?

User: I need email/password login with JWT tokens, plus Google OAuth, and basic user/admin roles.

Assistant: Perfect! Let me confirm:
- Feature: User authentication system
- Methods: Email/password + Google OAuth
- Tokens: JWT-based
- Roles: User and Admin
- Feature name: "user-authentication"

Is this correct?
User: Yes, that's exactly what I need.

[calls spec_driven_dev_goal_confirmed with feature_name="user-authentication"]

3. Requirements Gathering

Assistant: Great! Now let's gather detailed requirements.
[calls spec_driven_dev_requirements_start]

[Assistant generates detailed EARS-format requirements in .docs/specs/user-authentication/requirements.md]

User: The requirements look comprehensive. Approved!

[calls spec_driven_dev_requirements_confirmed]

4. Design Documentation

Assistant: Now let's create the technical design.
[calls spec_driven_dev_design_start]

[Assistant creates detailed design document with architecture, components, data models, etc.]

User: Design looks good, proceed!

[calls spec_driven_dev_design_confirmed]

5. Task Planning

Assistant: Let's break this down into executable tasks.
[calls spec_driven_dev_tasks_start]

[Assistant creates numbered task list with priorities and dependencies]

User: Task breakdown is perfect!

[calls spec_driven_dev_tasks_confirmed]

6. Task Execution

Assistant: Time to implement! Let's start coding.
[calls spec_driven_dev_execute_start]

[Assistant begins implementing tasks one by one, following the plan]

Generated Project Structure

.docs/specs/user-authentication/
├── requirements.md    # EARS-format requirements
├── design.md         # Technical architecture
└── tasks.md          # Implementation checklist

Development

Prerequisites

Make sure you have Node.js 18+ installed.

Setup

# Install dependencies with pnpm (recommended)
pnpm install

# Or with npm
npm install

Running the Project

# Development mode with pnpm
pnpm dev

# Or with npm
npm run dev

# Build with pnpm
pnpm build

# Or with npm
npm run build

# Start built version with pnpm
pnpm start

# Or with npm
npm start

# Test with pnpm (when available)
pnpm test

# Or with npm
npm test

License

MIT

Attribution

This project was inspired by and builds upon concepts from vibedevtools by @yinwm, a collection of development efficiency tools.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured