bmad-method-mcp

# Enhanced MCP Tools Plan for BMAD Method Integration ## Overview This document outlines advanced MCP tools to enhance the BMAD Method with intelligent AI-driven project management, automation, and quality assurance capabilities. ## Current MCP Tools (Implemented) 1. `bmad_create_story` - Create new stories with auto-numbering 2. `bmad_update_task_status` - Update task/story status and assignee 3. `bmad_get_next_story_number` - Get next available story number 4. `bmad_create_epic` - Create new epics 5. `bmad_query_tasks` - Query tasks with filters 6. `bmad_get_project_progress` - Get overall progress statistics 7. `bmad_create_document` - Create/update project documents 8. `bmad_create_sprint` - Create new sprints ## Enhanced MCP Tools - Phase 1: Intelligence & Automation ### 1. Story Analysis & Intelligence Tools #### `bmad_analyze_story_complexity` - **Purpose**: AI-powered story complexity analysis and estimation - **Parameters**: `story_id`, `analysis_type` (complexity, effort, risk) - **Returns**: Complexity score, effort estimate, risk factors, recommendations - **Integration**: Automatically analyzes stories when created/updated #### `bmad_suggest_story_breakdown` - **Purpose**: AI suggestions for breaking down complex stories - **Parameters**: `story_id`, `max_story_points` - **Returns**: Suggested sub-stories with acceptance criteria - **Integration**: Triggered when story complexity exceeds threshold #### `bmad_validate_acceptance_criteria` - **Purpose**: AI validation of story acceptance criteria completeness - **Parameters**: `story_id`, `criteria_text` - **Returns**: Validation score, missing elements, improvement suggestions - **Integration**: Real-time validation in story creation forms ### 2. Dependency & Workflow Management #### `bmad_detect_dependencies` - **Purpose**: Automatically detect dependencies between stories/epics - **Parameters**: `epic_num`, `include_external` (boolean) - **Returns**: Dependency graph, blocking relationships, critical path - **Integration**: Auto-runs when stories are created/modified #### `bmad_optimize_sprint_allocation` - **Purpose**: AI-powered sprint planning optimization - **Parameters**: `sprint_id`, `team_capacity`, `priority_weights` - **Returns**: Optimized story allocation, capacity utilization, risk assessment - **Integration**: Sprint planning assistant tool #### `bmad_suggest_next_tasks` - **Purpose**: Intelligent task recommendations based on project state - **Parameters**: `assignee`, `epic_num`, `context` - **Returns**: Prioritized task suggestions with reasoning - **Integration**: Personal task dashboard for each agent ### 3. Quality & Compliance Tools #### `bmad_review_code_quality` - **Purpose**: Automated code quality assessment integrated with tasks - **Parameters**: `task_id`, `code_repository`, `quality_gates` - **Returns**: Quality metrics, issues, recommendations, approval status - **Integration**: Automatic quality checks on task completion #### `bmad_validate_deliverables` - **Purpose**: AI validation of task deliverables against acceptance criteria - **Parameters**: `task_id`, `deliverable_urls`, `validation_type` - **Returns**: Validation results, missing items, quality score - **Integration**: Pre-completion validation workflow #### `bmad_check_compliance` - **Purpose**: Ensure project compliance with standards and regulations - **Parameters**: `project_scope`, `compliance_frameworks` - **Returns**: Compliance status, violations, remediation actions - **Integration**: Continuous compliance monitoring ## Enhanced MCP Tools - Phase 2: Advanced Analytics & Insights ### 4. Predictive Analytics Tools #### `bmad_predict_delivery_date` - **Purpose**: ML-powered delivery date prediction - **Parameters**: `epic_num`, `current_velocity`, `historical_data` - **Returns**: Predicted completion date, confidence interval, risk factors - **Integration**: Epic dashboard with predictive timeline #### `bmad_forecast_resource_needs` - **Purpose**: Predict resource requirements for upcoming work - **Parameters**: `time_horizon`, `project_scope`, `team_profile` - **Returns**: Resource forecasts, skill gap analysis, hiring recommendations - **Integration**: Resource planning dashboard #### `bmad_analyze_velocity_trends` - **Purpose**: Team velocity analysis and trend prediction - **Parameters**: `team_id`, `time_period`, `trend_analysis` - **Returns**: Velocity trends, performance insights, improvement recommendations - **Integration**: Team performance analytics ### 5. Risk Management Tools #### `bmad_assess_project_risks` - **Purpose**: Comprehensive project risk assessment - **Parameters**: `project_id`, `risk_categories`, `assessment_depth` - **Returns**: Risk register, impact analysis, mitigation strategies - **Integration**: Risk dashboard with automated monitoring #### `bmad_monitor_blockers` - **Purpose**: Proactive blocker detection and resolution tracking - **Parameters**: `epic_num`, `blocker_types`, `escalation_rules` - **Returns**: Active blockers, escalation recommendations, resolution suggestions - **Integration**: Real-time blocker alerts and dashboard #### `bmad_evaluate_technical_debt` - **Purpose**: Technical debt assessment and prioritization - **Parameters**: `codebase_url`, `debt_categories`, `business_impact` - **Returns**: Debt inventory, priority matrix, refactoring recommendations - **Integration**: Technical debt tracking in sprint planning ### 6. Communication & Collaboration Tools #### `bmad_generate_status_reports` - **Purpose**: Automated status report generation for stakeholders - **Parameters**: `report_type`, `audience`, `time_period`, `detail_level` - **Returns**: Formatted reports (PDF, HTML, Slack), key metrics, insights - **Integration**: Scheduled report generation and distribution #### `bmad_facilitate_retrospectives` - **Purpose**: AI-facilitated retrospective analysis and improvement suggestions - **Parameters**: `sprint_id`, `team_feedback`, `historical_retrospectives` - **Returns**: Retrospective insights, action items, improvement roadmap - **Integration**: Post-sprint retrospective automation #### `bmad_coordinate_handoffs` - **Purpose**: Intelligent coordination of work handoffs between agents - **Parameters**: `from_agent`, `to_agent`, `handoff_type`, `context` - **Returns**: Handoff checklist, context summary, quality gates - **Integration**: Workflow transition automation ## Enhanced MCP Tools - Phase 3: Advanced Integration & AI Agents ### 7. AI Agent Collaboration Tools #### `bmad_orchestrate_agent_workflow` - **Purpose**: Coordinate multi-agent workflows for complex tasks - **Parameters**: `workflow_type`, `participating_agents`, `coordination_rules` - **Returns**: Orchestration plan, agent assignments, communication protocols - **Integration**: Multi-agent task execution engine #### `bmad_negotiate_priorities` - **Purpose**: AI-mediated priority negotiation between agents - **Parameters**: `conflicting_priorities`, `business_constraints`, `negotiation_rules` - **Returns**: Consensus priorities, trade-off analysis, decision rationale - **Integration**: Priority conflict resolution system #### `bmad_share_knowledge` - **Purpose**: Cross-agent knowledge sharing and learning - **Parameters**: `knowledge_type`, `source_agent`, `target_agents`, `context` - **Returns**: Knowledge transfer plan, learning materials, validation tests - **Integration**: Continuous learning and knowledge management ### 8. External Integration Tools #### `bmad_sync_external_tools` - **Purpose**: Bidirectional sync with external project management tools - **Parameters**: `tool_type`, `sync_scope`, `mapping_rules`, `conflict_resolution` - **Returns**: Sync status, conflicts, data consistency reports - **Integration**: Real-time data synchronization with Jira, Azure DevOps, etc. #### `bmad_integrate_cicd_pipeline` - **Purpose**: Deep integration with CI/CD pipelines and deployment status - **Parameters**: `pipeline_url`, `integration_scope`, `notification_rules` - **Returns**: Pipeline status, deployment metrics, quality gates - **Integration**: DevOps workflow integration #### `bmad_connect_monitoring_tools` - **Purpose**: Integration with application monitoring and alerting systems - **Parameters**: `monitoring_tools`, `alert_routing`, `escalation_policies` - **Returns**: System health status, alert correlation, incident management - **Integration**: Production health monitoring in project context ## Implementation Strategy ### Phase 1 (Immediate): Intelligence & Automation - **Timeline**: 2-4 weeks - **Focus**: Core AI-powered analysis and automation tools - **Key Tools**: Story analysis, dependency detection, quality validation - **Benefits**: Immediate productivity gains, reduced manual effort ### Phase 2 (Medium-term): Analytics & Insights - **Timeline**: 1-2 months - **Focus**: Predictive analytics and risk management - **Key Tools**: Delivery prediction, resource forecasting, risk assessment - **Benefits**: Better planning, proactive issue resolution ### Phase 3 (Long-term): Advanced Integration - **Timeline**: 2-3 months - **Focus**: Multi-agent collaboration and external integrations - **Key Tools**: Agent orchestration, external tool sync, CI/CD integration - **Benefits**: Comprehensive project ecosystem integration ## Technical Implementation Notes ### MCP Server Enhancements Required 1. **Machine Learning Integration**: Add ML model serving capabilities 2. **External API Connectors**: Build connectors for popular tools (Jira, GitHub, etc.) 3. **Event-Driven Architecture**: Implement real-time event processing 4. **Advanced Data Analytics**: Add time-series analysis and predictive modeling 5. **Multi-Agent Communication**: Implement agent-to-agent communication protocols ### Database Schema Extensions 1. **Analytics Tables**: Store historical metrics and trends 2. **ML Model Metadata**: Track model versions and performance 3. **External Tool Mappings**: Maintain sync state and mappings 4. **Agent Collaboration**: Store agent interaction history and preferences ### WebUI Enhancements 1. **Analytics Dashboard**: Advanced charts and predictive visualizations 2. **Risk Management Interface**: Risk matrix and mitigation tracking 3. **Agent Collaboration Views**: Multi-agent workflow visualization 4. **Real-time Notifications**: Live updates and alert system ## Success Metrics ### Productivity Metrics - **Story Creation Time**: Reduce by 40% with AI assistance - **Planning Accuracy**: Improve estimation accuracy by 60% - **Quality Issues**: Reduce defects by 50% with automated validation ### Process Metrics - **Sprint Planning Time**: Reduce planning effort by 30% - **Risk Detection**: Identify 80% of risks proactively - **Communication Overhead**: Reduce status meetings by 50% ### Quality Metrics - **Acceptance Criteria Completeness**: Achieve 95% completeness score - **Technical Debt**: Maintain debt below 15% of codebase - **Delivery Predictability**: Achieve 90% on-time delivery rate ## Conclusion These enhanced MCP tools will transform the BMAD Method from a structured framework into an intelligent, self-optimizing project management ecosystem. The phased approach ensures immediate value while building toward comprehensive AI-driven project orchestration. The integration of these tools will enable: - **Proactive Problem Solving**: Issues detected and resolved before they impact delivery - **Intelligent Automation**: Routine tasks automated with AI-driven quality assurance - **Predictive Planning**: Data-driven forecasting and resource optimization - **Seamless Collaboration**: Frictionless handoffs and communication between agents - **Continuous Improvement**: Self-learning system that optimizes processes over time