bmap_mcp

# BMAD MCP Server A Model Context Protocol ([MCP](https://modelcontextprotocol.io/)) Server that exposes the [BMAD](https://github.com/bmadcode/BMAD-METHOD) (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**](./docs/index.md). ## Quick Start (Docker Recommended) 1. **Clone the repository:** ```bash 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: ```bash 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): ```bash docker-compose up bmad-mcp-server ``` The server will be available at `http://localhost:8000`. * For stdio mode: ```bash docker-compose up bmad-mcp-stdio ``` For local installation without Docker, see the [Getting Started](./docs/getting-started.md) 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](./docs/development-workflow.md) and [IDE Integration](./docs/ide-integration.md) documentation. Detailed configuration options for environment variables and server settings are available in the [Configuration](./docs/configuration.md) 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](LICENSE) for details. ## Related Projects - [BMAD Methodology](https://github.com/bmad-project/bmad-method) - Core BMAD framework - [CrewAI](https://github.com/crewAIInc/crewAI) - Multi-agent AI framework - [Model Context Protocol](https://modelcontextprotocol.io/) - Standard for AI tool integration