PRD Creator MCP Server

A specialized Model Context Protocol (MCP) server dedicated to creating Product Requirements Documents. This MCP server enables AI systems connected to MCP clients to generate detailed, well-structured product requirement documents through a standardized protocol interface.

Quick Start
Features
Installation
API Reference
Provider Configuration
Integrations
CLI Usage
Docker
Contributing
Changelog
Appendix

Quick Start

Via NPX (recommended):

npx -y prd-creator-mcp

Via Docker:

docker pull saml1211/prd-creator-mcp
docker run -i --rm saml1211/prd-creator-mcp

Configure Providers:

Copy .env.example to .env and set your API keys and preferred models.
Optionally, update provider credentials at runtime using the update_provider_config MCP tool.

Get Help:

npx prd-creator-mcp --help

Features

PRD Generator: Create complete PRDs based on product descriptions, user stories, and requirements
AI-Driven Generation: Generate high-quality PRDs using multiple AI providers
Multi-Provider Support: Choose from OpenAI, Google Gemini, Anthropic Claude, or local models
Provider Configuration: Customize provider options for each PRD generation
Fallback Mechanism: Gracefully falls back to template-based generation when AI is unavailable
PRD Validator: Validate PRD completeness against industry standards and customizable rule sets
Template Resources: Access a library of PRD templates for different product types
MCP Protocol Support: Implements the Model Context Protocol for seamless integration with MCP clients

Installation

Prerequisites

Node.js v16 or higher
npm or yarn

Install from source

Clone the repository:

git clone https://github.com/Saml1211/prd-mcp-server.git
cd prd-mcp-server

Install dependencies:

npm install

Build the project:

npm run build

Run locally:

npm start

For development with hot reload:

npm run dev

API Reference

The PRD Creator MCP Server provides the following tools:

`generate_prd`

Generate a complete PRD document using AI or template-based generation.

Parameters:

productName: The name of the product
productDescription: Description of the product
targetAudience: Description of the target audience
coreFeatures: Array of core feature descriptions
constraints (optional): Array of constraints or limitations
templateName (optional): Template name to use (defaults to "standard")
providerId (optional): Specific AI provider to use (openai, anthropic, gemini, local, template)
additionalContext (optional): Additional context or instructions for the AI provider
providerOptions (optional): Provider-specific options like temperature, maxTokens, etc.

Example:

{
  "productName": "TaskMaster Pro",
  "productDescription": "A task management application that helps users organize and prioritize their work efficiently.",
  "targetAudience": "Busy professionals and teams who need to manage multiple projects and deadlines.",
  "coreFeatures": [
    "Task creation and management",
    "Priority setting",
    "Due date tracking",
    "Team collaboration"
  ],
  "constraints": [
    "Must work offline",
    "Must support mobile and desktop platforms"
  ],
  "templateName": "comprehensive",
  "providerId": "openai",
  "additionalContext": "Focus on enterprise features and security",
  "providerOptions": {
    "temperature": 0.5,
    "maxTokens": 4000
  }
}

`validate_prd`

Validate a PRD document against best practices.

Parameters:

prdContent: The PRD content to validate
validationRules (optional): Array of validation rule IDs to check

Example:

{
  "prdContent": "# My Product\n\n## Introduction\n...",
  "validationRules": ["has-introduction", "minimum-length"]
}

`list_validation_rules`

List all available validation rules.

`list_ai_providers`

List all available AI providers and their availability status.

Example response:

[
  {
    "id": "openai",
    "name": "OpenAI",
    "available": true
  },
  {
    "id": "anthropic",
    "name": "Anthropic Claude",
    "available": false
  },
  {
    "id": "gemini",
    "name": "Google Gemini",
    "available": false
  },
  {
    "id": "local",
    "name": "Local Model",
    "available": false
  },
  {
    "id": "template",
    "name": "Template-based (No AI)",
    "available": true
  }
]

Template Management

The server provides additional tools for template management:

create_template: Create a new PRD template
list_templates: List all available templates
get_template: Get a specific template
update_template: Update an existing template
delete_template: Delete a template
export_templates: Export templates to JSON
import_templates: Import templates from JSON
render_template: Render a template with placeholders

System Management

get_provider_config: Get current provider configuration
update_provider_config: Update provider configuration
health_check: Check system health and provider availability
get_logs: Get recent system logs
stats: Get usage statistics

Provider Configuration & Hot Reload

Configuring AI Providers

You can configure provider credentials and models in two ways:

.env file: Place a .env file in your project or working directory. Use .env.example as a template. All standard AI provider variables (e.g., OPENAI_API_KEY, OPENAI_MODEL, etc.) are supported.
Live protocol tools: Update provider configuration at runtime using the update_provider_config tool via your MCP client. These changes are persisted and take effect immediately—no server restart required.

The server will always merge persistent config (from protocol tools) with environment variables, giving precedence to protocol/tool updates.

Hot Reload & Automation

When you update provider settings using either method, changes take effect instantly for all new requests. This enables:

Seamless automation and scripting via MCP tool interfaces
Hassle-free credential rotation and model switching
Dynamic environment support for CI/CD and cloud deployments

Integrations

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "prd-creator": {
      "command": "npx",
      "args": ["-y", "prd-creator-mcp"]
    }
  }
}

Glama.ai

Available at: https://glama.ai/mcp/servers/@Saml1211/PRD-MCP-Server

Cursor

Add to your Cursor MCP client configuration:

{
  "mcpServers": {
    "prd-creator": {
      "command": "npx",
      "args": ["-y", "prd-creator-mcp"]
    }
  }
}

Roo Code

Add to .roo/mcp.json:

{
  "mcpServers": {
    "prd-creator-mcp": {
      "command": "npx",
      "args": ["-y", "prd-creator-mcp"]
    }
  }
}

Cline

Reference prd-creator-mcp in your MCP workflow definitions.

CLI Usage

Install Globally (optional)

You may also install the MCP server globally to expose the CLI:

npm install -g prd-creator-mcp

Then run:

prd-creator-mcp

Command Reference

prd-creator-mcp Runs the MCP server (STDIO transport). Use directly via npx or as a globally installed CLI for integration with MCP clients and tools.

Uninstall

To remove the global CLI:

npm uninstall -g prd-creator-mcp

CLI Options

View available command line options:

npx prd-creator-mcp --help

Docker

Building the Docker image

docker build -t prd-creator-mcp .

Running with Docker

docker run -i --rm prd-creator-mcp

With environment variables

docker run -i --rm -e OPENAI_API_KEY=your_key_here prd-creator-mcp

Contributing

Please read CONTRIBUTING.md and CODE_OF_CONDUCT.md before submitting issues or pull requests.

Changelog

All notable changes to this project are documented in CHANGELOG.md.

Appendix

Useful Links

GitHub Repository
Model Context Protocol - Official MCP specification
MCP Inspector - Testing and debugging tool for MCP servers
NPM Package - Published npm package

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

A specialized Model Context Protocol server that enables AI systems to generate detailed, well-structured product requirement documents through a standardized interface.

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