draftlytic-mcp

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 reference

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.