Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
作者提供的说明(英文原文)
Offline MCP server for planning structured project specs — validate, render, and checklist tools.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"draftlytic": {
"command": "npx",
"args": [
"-y",
"draftlytic-mcp"
]
}
}
}在终端中运行这条命令
claude mcp add draftlytic -- npx -y draftlytic-mcp粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"draftlytic": {
"command": "npx",
"args": [
"-y",
"draftlytic-mcp"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"draftlytic": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"draftlytic-mcp"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日
注册名称已通过 GitHub 账户 rbsoftwaresystems 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(0.4.1)于4个月前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。
同一分类中有:Spec Driven Development, Remote Desktop Commander, Systemdox
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Your architecture in your agents' context: checks, specs, ADRs and docs over MCP, at write time.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
An MCP server that turns a rough project idea into a structured spec — right inside Claude Code, Cursor, or any MCP-compatible editor. No API key, no account, no network calls. It runs entirely on your machine and hands your editor's model a schema to write into, a checklist of what to ask about, a validator that catches gaps before you start coding, and a renderer that turns the result into a clean Markdown PRD.
This exists because "vibe coding" from a one-line prompt tends to produce a plausible-looking app that's missing half the decisions you actually needed to make — what's in scope for v1, what the data model looks like, what "done" means for a feature. draftlytic-mcp doesn't generate any of that for you; it structures the conversation so your model asks the right questions, then checks its own homework before you start building.
claude mcp add draftlytic -- npx -y draftlytic-mcp
Add to .cursor/mcp.json in your project (or the global ~/.cursor/mcp.json):
{
"mcpServers": { "draftlytic": { "command": "npx", "args": ["-y", "draftlytic-mcp"] } } }
Most MCP hosts read a generic mcp.json with the same shape:
{
"mcpServers": { "draftlytic": { "command": "npx", "args": ["-y", "draftlytic-mcp"] } } }
Once connected, ask your editor's model something like:
Use the plan_project prompt for "a habit tracker that reminds me by text message"
It'll walk through spec_checklist with you (platform, tech stack, audience, features, competitors, revenue, constraints, data model, notifications, external services, design & UX — a handful of concrete questions per category, many offered as click-to-pick single/multi-select choices rather than blank prompts, with a free-text escape always available), draft a spec, run it through validate_spec, fix what comes back, and hand you a rendered PRD in Markdown you can drop straight into a coding-agent prompt, a SPEC.md, or a GitHub issue.
You can also call the tools directly if you already have a spec drafted (by hand, or from another source) and just want it checked and rendered.
| Tool | Input | What it does |
|---|---|---|
validate_spec | spec (JSON object) | Zod-validates the spec and returns structured issues: errors for missing/empty required sections, placeholder text (TBD, lorem ipsum, fixme, etc.), and features without a priority — plus non-blocking quality hints like "no acceptance criteria on your must-haves" or "no non_goals listed". |
render_prd | spec (JSON object) | Renders a validated spec into deterministic Markdown: title, overview, target audience, platforms, tech stack, features grouped by priority with acceptance-criteria checklists, screens & navigation, data model tables, constraints, and non-goals. Refuses to render (returns an error) if the spec has structural errors. |
spec_checklist | — | Returns the planning checklist grouped by category, each with 2-4 concrete questions. Each question is { prompt, options?, multiSelect? } — questions with options are meant to be shown as selectable single/multi-choice answers (with a free-text escape), open ones stay free-text. |
open_in_draftlytic | spec (JSON object, optional) or idea (string) | Builds a link that opens your idea in the full Draftlytic app with the brief pre-filled — its guided AI question flow, richer generation, an editable spec editor, and PRD export live there. Compresses a spec (even a partial one) into a starting brief, or takes a plain-text idea. Builds the URL locally; sends nothing anywhere. |
Plus one prompt:
| Prompt | Args | What it does |
|---|---|---|
plan_project | idea (string) | Instructs the model to interview the user with spec_checklist, draft a spec, validate and fix it in a loop, then render the final PRD. |
{
name: string overview: string target_audience: string platforms: string[] tech_stack: string[] features: Array<{ title: string description: string priority: "must-have" | "nice-to-have" | "future" acceptance_criteria?: string[] }> screens?: Array<{ name: string; purpose: string }> data_model?: Array<{ entity: string fields: Array<{ name: string; type: string; notes?: string }> }> constraints?: string[] non_goals?: string[] revenue_model?: string }
validate_spec and spec_checklist are heuristics, not a substitute for actually knowing what you're building. A spec that passes validation can still be a bad plan.TBD/lorem ipsum/fixme-style filler, not "this description is vague but technically real words."draftlytic-mcp is the offline sibling of draftlytic.com — the full editor adds AI generation, logo drafts, scan-for-gaps, and GitHub push.