### **Product Requirements Document (PRD): MCP Project Initializer**
**1. Introduction**
The process of starting a new AI-powered software project involves significant boilerplate and setup. Developers must manually gather context files, create documentation, and establish best practices. This manual process is slow, repetitive, and error-prone. The **MCP Project Initializer** is a command-line MCP server that automates this entire workflow. It acts as a conversational guide, interacting with the developer through any standard MCP client to set up a new project with all the necessary context, rules, and initial documentation, ready for AI-assisted development.
**2. User Persona**
* **Name:** Alex, the AI-Native Developer
* **Role:** Software Engineer who uses AI assistants (like Copilot, Cursor, or other IDE integrations) as a core part of their development process.
* **Goals:**
* To start new projects quickly without tedious setup.
* To ensure the AI has the best possible context from day one to generate high-quality, relevant code.
* To standardize project structure and documentation practices across their work.
* **Frustrations:**
* Forgetting to add crucial context files (`.cursorrules`, style guides) to new projects.
* Wasting time creating boilerplate documentation like a PRD.
* The "cold start" problem where the AI has no project-specific information to work with.
**3. Core Features & User Stories**
* **F1: Conversational Project Initialization**
* **User Story:** As Alex, I want to type a simple command like `/init-mcp` in my chat client so that I can trigger the automated setup workflow without leaving my editor.
* **F2: Capturing Project Intent**
* **User Story:** As Alex, I want the tool to ask me for a high-level description of my project so that this intent can be used to generate relevant documentation later.
* **F3: Technology-Specific Context Fetching**
* **User Story:** As Alex, I want to specify my primary technology (e.g., TypeScript) so that the tool automatically downloads and saves the relevant best-practice rules (e.g., from `awesome-cursorrules`) and SDK documentation into my project.
* **F4: Custom Documentation Ingestion**
* **User Story:** As Alex, I want the tool to allow me to provide URLs to existing documentation (like an OpenAPI spec) or paste raw text so that all relevant external context is included in my project.
* **F5: LLM-Powered PRD Generation**
* **User Story:** As Alex, I want the tool to use all the gathered context to generate a high-quality prompt for my own LLM, which will then create a detailed PRD, saving me the effort of writing it myself.
* **F6: Local File System Operations**
* **User Story:** As a developer running a local tool, I expect the server to be able to create directories (e.g., `/docs`, `/.windsurf`) and save all the gathered files and generated documents directly to my project's file system.
**4. Technical Requirements & Constraints**
* **Language:** TypeScript
* **Runtime:** Node.js (LTS version)
* **Protocol:** Model Context Protocol (MCP) communicating over **STDIO**.
* **Core Dependency:** The official `@modelcontextprotocol/sdk` TypeScript SDK.
* **External Dependencies:** A library for making HTTP requests (e.g., `axios`).
* **LLM Interaction:** The server **must not** have its own LLM API key. It must use the "Prompt Server / Response" pattern (`mcp_prompt_request` and `mcp_llm_response`) to leverage the client's connected LLM.
* **Execution:** The final script should be executable directly from the command line.
**5. Out of Scope for Version 1.0**
* A graphical user interface (GUI).
* Support for TCP server communication.
* A pre-configured library of rules for more than 2-3 initial languages (e.g., TypeScript, Python).
* Complex error recovery (e.g., retrying failed downloads).
