spec-driver-mcp

spec-driver-mcp

Enables AI coding tools to follow a structured spec-driven development workflow with three phases: requirements, design, and tasks, ensuring approval before advancing.

Category
Visit Server

README

Spec Driver MCP

English | 中文 | Русский

A Model Context Protocol server that brings Kiro-style spec-driven development to any AI coding tool (Claude Code, Cursor, opencode, Codex CLI, Reasonix, Gemini CLI, and more).

What it does

Replaces unstructured chat with a 3-phase workflow when you ask AI to build, plan, or refactor a project:

Phase 1: Requirements  →  requirements.md (EARS format: WHEN...THE SYSTEM SHALL...)
Phase 2: Design         →  design.md (architecture, components, data flow)
Phase 3: Tasks          →  tasks.md (trackable [x] checkboxes)

Each phase requires your approval before advancing. Hooks auto-maintain consistency.

Quick Start

1. Install

npx github:linanwanttodo/spec-driver-mcp

Or install globally:

npm install -g github:linanwanttodo/spec-driver-mcp

2. Add to your AI tool

opencode

Add to ~/.config/opencode/opencode.json:

{
  "mcp": {
    "spec-driver": {
      "type": "local",
      "command": ["npx", "github:linanwanttodo/spec-driver-mcp"]
    }
  }
}

Claude Code

Add to ~/.claude/settings.json:

{
  "mcpServers": {
    "spec-driver": {
      "command": "npx",
      "args": ["github:linanwanttodo/spec-driver-mcp"]
    }
  }
}

Then copy instructions/CLAUDE.md to your project root as CLAUDE.md.

Cursor

Create .cursor/mcp.json in your project:

{
  "mcpServers": {
    "spec-driver": {
      "command": "npx",
      "args": ["github:linanwanttodo/spec-driver-mcp"]
    }
  }
}

Then copy instructions/cursor-rules.md to your project root as .cursorrules.

Codex CLI

Add to ~/.codex/config.toml:

[mcp_servers.spec-driver]
command = "npx"
args = ["github:linanwanttodo/spec-driver-mcp"]

Reasonix

Add to ~/.reasonix/config.json mcp array:

"spec-driver=npx github:linanwanttodo/spec-driver-mcp"

Gemini CLI

Add to ~/.gemini/config/mcp_config.json:

{
  "mcpServers": {
    "spec-driver": {
      "command": "npx",
      "args": ["github:linanwanttodo/spec-driver-mcp"]
    }
  }
}

Usage

Once configured, just tell your AI:

"帮我开发一个博客系统" or "帮我分析这个项目" or "我们来重构这个"

The AI will automatically:

  1. Read your codebase to understand context
  2. Ask you clarifying questions (requirements-first or design-first? specific details?)
  3. Write requirements.md using EARS notation
  4. Wait for your approval before proceeding
  5. Write design.md with architecture and component design
  6. Wait for your approval
  7. Write tasks.md with - [ ] checkboxes
  8. Wait for your approval, then implement

Marking tasks done

During implementation:

- [x] Task completed via update-task
- [ ] Still pending

The AI auto-runs hooks at each event:

  • on-task-completed → auto-mark task [x], check acceptance criteria
  • on-spec-phase-change → validate document completeness
  • on-user-request-change → sync all documents

Tools

Tool Description
init-spec Initialize .spec/ with config + 3 default hooks
write-spec-file Write requirements.md / design.md / tasks.md
read-spec-file Read any spec file
list-spec-files Show file status + hooks
update-task Toggle task [x] or [ ]
get-task-summary Task completion stats
create-hook Create automation rules
list-hooks List all hooks
delete-hook Remove a hook
run-hooks Execute hooks for an event

Spec File Location

All files are created in .spec/ at your project root:

your-project/
├── .spec/
│   ├── .config              # Project metadata
│   ├── requirements.md       # Phase 1: Requirements
│   ├── design.md             # Phase 2: Design
│   ├── tasks.md              # Phase 3: Tasks
│   └── hooks/                # Automation rules
│       ├── auto-mark-completed.md
│       ├── validate-before-phase.md
│       └── notify-on-change.md
├── ...your code...

Default Hooks

Hook Event What it does
auto-mark-completed on-task-completed Auto update-task after implementation
validate-before-phase on-spec-phase-change Validate doc completeness before advancing
notify-on-change on-user-request-change Sync all docs when requirements change

One-Click AI Install Prompt

If you're asking an AI to set this up for you, copy this:

Install spec-driver-mcp from GitHub and configure it:
1. Run: npm install -g github:linanwanttodo/spec-driver-mcp
2. Add "spec-driver" to the MCP servers config of my AI tool
   (the config format depends on which tool I use)
3. Done - no server needed, no API keys, no registration

License

MIT

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