What is Bmap Mcp

bmap_mcp is a Model Context Protocol (MCP) Server that implements the BMAD methodology, providing standardized tools for AI-driven project development.

Use cases

Use cases for bmap_mcp include generating project briefs, designing architectures, managing user stories, and ensuring quality assurance in AI-driven projects.

How to use

To use bmap_mcp, clone the repository, configure your environment variables with your API keys, and run the server using Docker Compose.

Key features

Key features include 9 BMAD tools for development, CrewAI integration for complex reasoning, dual transport support for local and web clients, template compliance for generated artifacts, built-in validation with BMAD checklists, and Docker readiness for easy deployment.

Where to use

bmap_mcp can be used in various fields such as software development, project management, and AI system integration, particularly where structured workflows are beneficial.

BMAD MCP Server

A Model Context Protocol (MCP) Server that exposes the BMAD (Breakthrough Method of Agile AI-driven Development) methodology as standardized tools for AI systems.

Overview

The BMAD MCP Server bridges the proven BMAD methodology with the broader AI ecosystem through the Model Context Protocol standard. It enables any MCP-compatible AI system to leverage structured project development workflows including:

• Project Planning: Generate project briefs and comprehensive PRDs.
• Architecture Design: Create technical and frontend architectures.
• Story Management: Generate and validate development-ready user stories.
• Quality Assurance: Run BMAD checklists and validation tools.

Features

• 🔧 9 BMAD Tools: A comprehensive toolkit for AI-driven development.
• 🚀 CrewAI Integration: Powered by collaborative AI agents for complex reasoning.
• 📡 Dual Transport: Supports both stdio (for local clients) and Server-Sent Events (SSE for web-based clients).
• ✅ Template Compliance: All generated artifacts adhere to BMAD methodology templates.
• 🔍 Built-in Validation: Quality checks using BMAD checklists ensure high-quality outputs.
• 🐳 Docker Ready: Easy deployment and scaling with Docker and Docker Compose.

Documentation

For detailed setup, configuration, usage examples, and development workflow, please refer to our full Documentation.

Quick Start (Docker Recommended)

1. Clone the repository:

   git clone https://github.com/bmad-project/bmad-mcp-server
   cd bmad-mcp-server
   

2. Configure environment variables:
   Copy the example environment file and edit it with your API keys:

   cp .env.example .env
   # Open .env and add your LLM API keys (OpenAI, Anthropic, Google Gemini, AWS Bedrock)
   

3. Run with Docker Compose:
   • For SSE mode (recommended for most clients):

     docker-compose up bmad-mcp-server
     

     The server will be available at http://localhost:8000.
   • For stdio mode:

     docker-compose up bmad-mcp-stdio
     

For local installation without Docker, see the Getting Started guide in our documentation.

Available Tools (Summary)

Project Planning Tools

• create_project_brief - Generate structured project briefs
• generate_prd - Create comprehensive PRDs with epics and stories
• validate_requirements - Check PRDs against PM quality standards

Architecture Tools

• create_architecture - Generate technical architecture documents
• create_frontend_architecture - Design frontend-specific architectures
• validate_architecture - Check architectures against quality standards (Note: This tool is conceptual, run_checklist with architect_checklist.md or frontend_architecture_checklist.md serves this purpose)

Story Management Tools

• create_next_story - Generate development-ready user stories
• validate_story - Check stories against Definition of Done

Quality Tools

• run_checklist - Execute any BMAD checklist against documents
• correct_course - Handle change management scenarios

Documentation

For detailed usage examples with various AI assistants (Cline, Claude Code, GitHub Copilot) and direct MCP client interactions, please see our Development Workflow and IDE Integration documentation.

Detailed configuration options for environment variables and server settings are available in the Configuration documentation.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes following the coding standards
4. Add tests for new functionality
5. Submit a pull request

License

MIT License - see LICENSE for details.

Related Projects

• BMAD Methodology - Core BMAD framework
• CrewAI - Multi-agent AI framework
• Model Context Protocol - Standard for AI tool integration