draftlytic-mcp
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.
Install
Claude Code
claude mcp add draftlytic -- npx -y draftlytic-mcp
Cursor
Add to .cursor/mcp.json in your project (or the global ~/.cursor/mcp.json):
{
"mcpServers": {
"draftlytic": {
"command": "npx",
"args": ["-y", "draftlytic-mcp"]
}
}
}
Any other MCP client
Most MCP hosts read a generic mcp.json with the same shape:
{
"mcpServers": {
"draftlytic": {
"command": "npx",
"args": ["-y", "draftlytic-mcp"]
}
}
}
Usage
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. |
The spec shape
{
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
}
Honest limits
- This is v1 and purely local. There's no Draftlytic API behind it — every tool runs synchronous, offline logic against whatever spec JSON your editor's model hands it. It doesn't call any AI itself.
- The model does the writing, this just structures it.
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.
- Placeholder detection is pattern-based, not semantic. It catches
TBD/lorem ipsum/fixme-style filler, not "this description is vague but technically real words."
- No persistence. Nothing is saved between calls — the spec JSON lives in the conversation. If you want it saved, ask your model to write it to a file.
- No collaboration, no versioning, no export formats beyond Markdown. It's a planning tool, not a project manager.
draftlytic-mcp is the offline sibling of draftlytic.com — the full editor adds AI generation, logo drafts, scan-for-gaps, and GitHub push.