DollhouseMCP

--- name: "Project Context" description: "Persistent memory for project-specific information, decisions, and history" type: "memory" version: "1.0.0" author: "DollhouseMCP" created: "2025-07-23" category: "project" tags: ["context", "project", "history", "decisions", "knowledge-base"] storage_backend: "file" retention_policy: default: "perpetual" rules: - type: "decisions" retention: "perpetual" - type: "daily_updates" retention: "90 days" - type: "meeting_notes" retention: "1 year" - type: "technical_details" retention: "perpetual" privacy_level: "project-internal" searchable: true schema: project_info: type: "object" properties: name: "string" description: "string" start_date: "date" team_members: "array" tech_stack: "array" repositories: "array" decisions: type: "array" items: date: "date" decision: "string" rationale: "string" participants: "array" technical_notes: type: "object" properties: architecture: "object" dependencies: "array" api_contracts: "object" known_issues: "array" _dollhouseMCPTest: true _testMetadata: suite: "bundled-test-data" purpose: "General test data for DollhouseMCP system validation" created: "2025-08-20" version: "1.0.0" migrated: "2025-08-20T23:47:24.343Z" originalPath: "data/memories/project-context.md" --- # Project Context Memory This memory element maintains comprehensive project knowledge including technical decisions, team information, architecture details, and historical context. ## Memory Structure ### 1. Project Information ```yaml project: name: "{{project_name}}" description: "{{project_description}}" start_date: "{{start_date}}" status: "{{status}}" phase: "{{current_phase}}" team: lead: "{{team_lead}}" members: - name: "{{member_name}}" role: "{{role}}" expertise: ["{{skill1}}", "{{skill2}}"] availability: "{{availability}}" stakeholders: - name: "{{stakeholder_name}}" role: "{{stakeholder_role}}" interest: "{{interest_level}}" ``` ### 2. Technical Context ```yaml architecture: pattern: "{{architecture_pattern}}" components: frontend: framework: "{{frontend_framework}}" version: "{{version}}" key_libraries: ["{{lib1}}", "{{lib2}}"] backend: language: "{{backend_language}}" framework: "{{backend_framework}}" database: "{{database_type}}" infrastructure: hosting: "{{hosting_platform}}" ci_cd: "{{ci_cd_tool}}" monitoring: "{{monitoring_solution}}" api_endpoints: - path: "/api/v1/{{resource}}" method: "{{http_method}}" description: "{{endpoint_description}}" authentication: "{{auth_type}}" integrations: - service: "{{service_name}}" purpose: "{{integration_purpose}}" status: "{{integration_status}}" ``` ### 3. Decision Log ```yaml decisions: - date: "2025-07-20" category: "architecture" decision: "Use microservices architecture" rationale: "Need independent scaling and deployment" impact: "high" participants: ["John", "Sarah", "Mike"] alternatives_considered: - option: "Monolithic" rejected_because: "Scaling limitations" - option: "Serverless" rejected_because: "Vendor lock-in concerns" follow_up: "Review after 6 months" - date: "2025-07-21" category: "technology" decision: "Adopt TypeScript for all new code" rationale: "Type safety and better IDE support" impact: "medium" participants: ["Dev Team"] migration_plan: "Gradual migration over Q3" ``` ### 4. Knowledge Base ```yaml common_issues: - issue: "Database connection timeout" symptoms: ["API returns 504", "Logs show connection pool exhausted"] root_cause: "Connection pool size too small" solution: "Increased pool size to 50" prevention: "Monitor connection usage metrics" coding_conventions: - category: "naming" convention: "Use camelCase for variables" examples: ["userName", "getProductList"] - category: "file_structure" convention: "Feature-based organization" structure: "src/features/[feature-name]/" deployment_procedures: staging: steps: 1: "Run test suite" 2: "Build Docker image" 3: "Deploy to staging cluster" 4: "Run smoke tests" rollback: "kubectl rollout undo deployment/app-staging" production: approval_required: true steps: 1: "Get approval from PM" 2: "Create release tag" 3: "Deploy using blue-green strategy" 4: "Monitor for 30 minutes" ``` ## Memory Operations ### 1. Context Retrieval ``` Query: "What database are we using?" Response: "PostgreSQL 14.2 on AWS RDS, chosen for JSONB support and proven scalability" Query: "Why did we choose React?" Response: "Decision made on 2025-07-15: React chosen for component reusability, large ecosystem, and team expertise. Vue.js was considered but team lacked experience." ``` ### 2. Historical Analysis ``` Query: "What decisions were made about authentication?" Response: - 2025-07-18: OAuth2 with JWT tokens - 2025-07-25: Added 2FA for admin users - 2025-08-02: Implemented refresh token rotation ``` ### 3. Team Knowledge ``` Query: "Who knows about the payment system?" Response: "Sarah (primary), Mike (secondary). Sarah implemented the Stripe integration and Mike did the refund logic." ``` ## Integration Patterns ### Auto-Population Sources 1. **Meeting Notes**: Extracts decisions and action items 2. **Code Commits**: Updates technical details 3. **Documentation**: Syncs architecture changes 4. **Chat History**: Captures informal decisions ### Conflict Resolution When conflicting information is detected: 1. More recent information takes precedence 2. Information from authoritative sources (PM, Tech Lead) weighted higher 3. Conflicts logged for human review ## Privacy & Access Control ### Access Levels - **Public**: General project information - **Team**: Technical details, non-sensitive decisions - **Restricted**: Security details, personnel information - **Confidential**: Financial data, strategic plans ### Data Retention ```yaml retention_rules: immediate_purge: - passwords - api_keys - personal_emails short_term: - daily_standups: 30_days - debug_logs: 7_days long_term: - architectural_decisions: permanent - post_mortems: 2_years - release_notes: 1_year ``` ## Search Capabilities ### Natural Language Queries - "What did we decide about caching?" - "Show me all security-related decisions" - "Who worked on the user authentication?" - "What were the reasons for choosing MongoDB?" ### Structured Queries ```yaml search: type: "decision" category: "architecture" date_range: from: "2025-01-01" to: "2025-07-31" participants: ["Sarah"] ``` ## Learning & Adaptation ### Pattern Recognition - Identifies recurring issues - Suggests solutions based on past fixes - Alerts when similar problems arise - Tracks decision outcomes ### Knowledge Graph Builds relationships between: - People and their expertise areas - Decisions and their outcomes - Problems and their solutions - Components and their dependencies