DinCoder

# DinCoder Feature Gap Analysis & Roadmap **Analysis Date:** 2025-10-04 **Updated:** 2025-10-16 (Phase 1 Completion) **Current Version:** v0.2.0 **Project Status:** 22/23 Stories Complete (96%) --- > ## 🎉 **PHASE 1 COMPLETE** (Updated 2025-10-16) > > All critical gaps identified in this analysis have been **successfully addressed** in v0.2.0! > > **✅ Completed Features:** > - `constitution_create` - Project principles & constraints > - `clarify_add` / `clarify_resolve` / `clarify_list` - Structured Q&A workflow > - `spec_validate` - Quality gates with 4 validation rules > - `artifacts_analyze` - Cross-artifact consistency checking > - `spec_refine` - Iterative spec improvement by section > - `prereqs_check` - Environment validation (Node.js, npm, git, custom) > > **Result:** 100% GitHub Spec Kit command parity achieved! 🏆 > > See [CHANGELOG.md](../CHANGELOG.md) v0.2.0 for complete details. --- ## Executive Summary This document analyzes the gap between GitHub's official Spec Kit and our DinCoder MCP server, identifies MCP-appropriate features from community discussions, and proposes a roadmap for supercharging DinCoder as an AI-assisted coding companion. **Key Finding:** DinCoder has strong foundational tools but is missing critical workflow commands that would make it a complete Spec-Driven Development solution for AI coding assistants. --- ## Current State Analysis ### ✅ What DinCoder Has (14 Tools) #### Core Workflow Tools (5) 1. **specify_start** - Initialize new spec-driven project 2. **specify_describe** - Create/update project specification 3. **plan_create** - Generate technical implementation plan 4. **tasks_generate** - Create actionable task list 5. **tasks_tick** - Mark tasks as complete #### Supporting Tools (3) 6. **artifacts_read** - Read generated specs/plans/tasks 7. **research_append** - Document technical decisions 8. **git_create_branch** - Create feature branches #### Quality Tools (6) 9. **quality_format** - Code formatting 10. **quality_lint** - Linting 11. **quality_test** - Run tests 12. **quality_security_audit** - Security scanning 13. **quality_deps_update** - Update dependencies 14. **quality_license_check** - License compliance ### ❌ What's Missing from Official Spec Kit Comparing to GitHub Spec Kit commands: | Spec Kit Command | DinCoder Equivalent | Status | |-----------------|---------------------|--------| | `/specify init` | `specify_start` | ✅ Implemented | | `/specify check` | None | ❌ **Missing** | | `/constitution` | None | ❌ **Missing** | | `/specify` | `specify_describe` | ✅ Implemented | | `/clarify` | None | ❌ **Missing** | | `/plan` | `plan_create` | ✅ Implemented | | `/tasks` | `tasks_generate` | ✅ Implemented | | `/analyze` | None | ❌ **Missing** | | `/implement` | None | ❌ **Missing** (See Note) | **Note on /implement:** This command is intentionally NOT suitable for MCP servers. In Spec Kit, `/implement` is executed by the AI agent (Claude, Copilot, etc.) reading the tasks.md and writing code. In an MCP context, the AI agent (Claude Code) IS the implementer, so this becomes circular. DinCoder correctly focuses on GENERATING the artifacts that guide implementation, not doing the implementation itself. --- ## Gap Analysis: Core Missing Features ### 🔴 Critical Gaps (High Priority) #### 1. **Constitution/Principles Tool** (`constitution_create`) **What It Does:** Define project principles and constraints before specification **Why It Matters:** - Provides guardrails for AI-generated specs and plans - Establishes coding standards, architectural patterns, preferred libraries - Prevents scope creep and maintains consistency **MCP Implementation:** ```typescript // Tool: constitution_create { projectName: string, principles: string[], // e.g., ["Prefer functional patterns", "No class components"] constraints: string[], // e.g., ["Max bundle size: 500KB", "Support Node 20+"] preferences: { libraries?: string[], // e.g., ["React Query over Redux"] patterns?: string[], // e.g., ["Repository pattern for data access"] style?: string // e.g., "Functional > OOP" } } ``` **User Value:** AI assistants can reference constitution when generating specs/plans/tasks to ensure alignment with project values. --- #### 2. **Clarification Tool** (`clarify_add` / `clarify_resolve`) **What It Does:** Flag ambiguities in specs and track their resolution **Why It Matters:** - Specs often have [NEEDS CLARIFICATION] markers - AI needs structured way to identify and resolve uncertainties - Prevents implementing wrong assumptions **MCP Implementation:** ```typescript // Tool: clarify_add { question: string, // "Should we support OAuth or just API keys?" context: string, // Section of spec that's ambiguous options?: string[] // Possible resolutions } // Tool: clarify_resolve { clarificationId: string, resolution: string, rationale?: string } ``` **User Value:** Structured Q&A process ensures specs are complete before planning/implementation. --- #### 3. **Analyze/Validate Tool** (`spec_validate` / `artifacts_analyze`) **What It Does:** Check specification completeness and consistency **Why It Matters:** - Catches missing acceptance criteria - Detects contradictions between spec/plan/tasks - Ensures WHAT/WHY separation (no premature HOW in specs) **MCP Implementation:** ```typescript // Tool: spec_validate { checkFor: { completeness: boolean, // All sections present? acceptanceCriteria: boolean, // Every feature has tests? clarifications: boolean, // Any unresolved [NEEDS CLARIFICATION]? prematureImplementation: boolean // HOW details in WHAT section? } } // Returns validation report with warnings/errors ``` **User Value:** Quality gates before moving to next phase. AI can auto-fix common issues. --- #### 4. **Task Dependency Visualization** (`tasks_visualize`) **What It Does:** Generate task dependency graphs **Why It Matters:** - Shows critical path and parallelizable work - Helps AI understand which tasks to tackle first - Identifies blockers **MCP Implementation:** ```typescript // Tool: tasks_visualize { format: 'mermaid' | 'dot' | 'json', includeEstimates: boolean, highlightCriticalPath: boolean } // Returns Mermaid diagram code or DOT graph ``` **User Value:** AI can suggest optimal task ordering. Visual representation aids human review. --- ### 🟡 Important Gaps (Medium Priority) #### 5. **Spec Refinement/Evolution** (`spec_refine`) **What It Does:** Iteratively improve existing specs based on learnings **Why It Matters:** - Specs evolve as understanding deepens - Need structured way to update without losing history - Track decision changes **MCP Implementation:** ```typescript // Tool: spec_refine { section: 'goals' | 'acceptance' | 'edge-cases' | 'full', changes: string, // Markdown describing refinements reason: string // Why this refinement is needed } ``` **User Value:** Living specs that improve over time. Git commits track evolution. --- #### 6. **Template Customization** (`templates_customize`) **What It Does:** Override default Spec Kit templates **Why It Matters:** - Different projects need different sections - Teams may have custom workflows - Support domain-specific templates (web vs CLI vs mobile) **MCP Implementation:** ```typescript // Tool: templates_customize { templateType: 'spec' | 'plan' | 'tasks', customSections?: string[], // Additional sections to include templatePath?: string // Path to custom template file } ``` **User Value:** Flexibility for teams with established processes. --- #### 7. **Contract Generation** (`contracts_generate`) **What It Does:** Auto-generate API contracts from spec.md **Why It Matters:** - Specs describe APIs at high level - Need machine-readable contracts (OpenAPI, GraphQL schema, Protobuf) - Enforce contract-first development **MCP Implementation:** ```typescript // Tool: contracts_generate { contractType: 'openapi' | 'graphql' | 'protobuf' | 'typescript', source: 'spec.md', outputPath?: string } ``` **User Value:** AI can generate contract stubs, validate implementations against contracts. --- #### 8. **Prerequisites Check** (`prereqs_check`) **What It Does:** Verify system has required tools/dependencies **Why It Matters:** - Similar to `specify check` in official CLI - Prevents "works on my machine" issues - Validates environment before spec execution **MCP Implementation:** ```typescript // Tool: prereqs_check { checkFor: { node?: string, // e.g., ">=20" npm?: boolean, git?: boolean, customCommands?: string[] // e.g., ["docker", "kubectl"] } } ``` **User Value:** Early detection of environment issues. AI can suggest fixes. --- ### 🟢 Nice-to-Have Gaps (Lower Priority) #### 9. **Idea Capture** (`idea_capture`) ❌ NOT NEEDED - REDUNDANT **Status:** REMOVED from roadmap **Research Finding:** GitHub Spec Kit does NOT have `/idea` command - `/idea` is just a discussion proposal (#679), not implemented - Spec Kit's `/specify` already accepts terse input like "Build a todo app" - Our `specify_describe` works identically **Conclusion:** - ✅ `specify_describe` already does this - ❌ No separate tool needed - 📝 Better document that `specify_describe` handles terse to detailed input --- #### 10. **Task Range Operations** (`tasks_tick_range`) **What It Does:** Mark multiple tasks complete at once **Why It Matters:** - Efficiency when batch-implementing related tasks - Reduce tool call overhead **MCP Implementation:** ```typescript // Tool: tasks_tick_range { taskIds: string[], // ["T001", "T002", "T003"] notes?: string // Optional completion notes } ``` **User Value:** Faster workflow for AI when implementing multiple small tasks. --- #### 11. **Metrics & Analytics** (`metrics_report`) **What It Does:** Track project velocity and spec quality **Why It Matters:** - How many specs completed? - Average tasks per spec? - Spec-to-implementation time? **MCP Implementation:** ```typescript // Tool: metrics_report { timeRange?: string, // "last-30-days" metrics: ['velocity', 'quality', 'cycle-time'] } ``` **User Value:** Data-driven insights for improving SDD process. --- ## Features That DON'T Translate to MCP ### ❌ Collaboration Features - **Why Not:** MCP servers are single-user, single-session. Multi-user coordination requires external systems (GitHub, Linear, etc.) - **Examples:** Team assignment, code review orchestration, PR workflows ### ❌ External Tool Integration (as separate feature) - **Why Not:** MCP IS the integration layer. External tools should be separate MCP servers that compose together. - **Examples:** Slack notifications, JIRA sync, deployment triggers ### ❌ IDE-Specific Features - **Why Not:** MCP is IDE-agnostic. IDE features belong in IDE extensions. - **Examples:** Syntax highlighting rules, keybindings, UI panels --- ## Community Ideas from GitHub Discussions ### 💡 High-Value Ideas (MCP-Appropriate) 1. **Visual Diagrams in Artifacts** ⭐⭐⭐ - **Idea:** Embed Mermaid/PlantUML diagrams in spec.md, plan.md - **MCP Fit:** Perfect. AI can generate diagrams, render as markdown - **Implementation:** Add `diagram_generate` tool 2. **Specialized Sub-Agents** ⭐⭐ - **Idea:** Different AI agents for different task types (frontend, backend, tests) - **MCP Fit:** Moderate. Could be task metadata: `taskType: 'frontend' | 'backend'` - **Implementation:** Add `taskType` field to tasks, AI can route accordingly 3. **Task Tracking by Phase** ⭐⭐⭐ - **Idea:** Group tasks by implementation phase (setup, core, polish) - **MCP Fit:** Excellent. Natural extension of tasks.md - **Implementation:** Add `phase` field to tasks, `tasks_filter` tool 4. **One-Shot Project Bootstrapping** ⭐⭐ - **Idea:** `/idea "Build X"` → Full project setup - **MCP Fit:** Good. Combines `idea_capture` + `specify_describe` + `plan_create` + `tasks_generate` - **Implementation:** Add `project_bootstrap` orchestrator tool 5. **i18n/Locale Support** ⭐ - **Idea:** Generate specs in different languages - **MCP Fit:** Low priority but feasible - **Implementation:** Add `locale` parameter to template tools ### 🚫 Low-Value Ideas (Not MCP-Appropriate) 1. **Visual Studio Integration** - IDE-specific, not MCP concern 2. **Scaffolding Code Generation** - `/implement` is AI agent's job, not MCP server 3. **Changing AI Agents Mid-Project** - User's choice, not tool concern --- ## Proposed Roadmap: Supercharging DinCoder ### Phase 1: Core Completeness (v0.2.0) 🎯 **Goal:** Match essential Spec Kit functionality for AI coding assistants 1. ✅ **constitution_create** - Define project principles 2. ✅ **clarify_add** / **clarify_resolve** - Track spec ambiguities 3. ✅ **spec_validate** - Quality gates for specs 4. ✅ **spec_refine** - Iterative spec improvement 5. ✅ **prereqs_check** - Environment validation **Estimated Effort:** 5 stories × 3-5 tasks each = ~20 tasks (~1 sprint) --- ### Phase 2: Workflow Enhancement (v0.3.0) 🚀 **Goal:** Improve AI workflow efficiency 6. ✅ **tasks_visualize** - Dependency graphs 7. ✅ **tasks_filter** - Filter by phase/type/status 8. ✅ **tasks_tick_range** - Batch task completion 9. ❌ ~~**diagram_generate**~~ - REMOVED (AI writes Mermaid directly) 10. ❌ ~~**idea_capture**~~ - REMOVED (redundant with specify_describe) **Estimated Effort:** 3 stories × 3-5 tasks each = ~12 tasks (~1 sprint) **Updated:** Removed 2 redundant features based on Spec Kit research --- ### Phase 3: Advanced Features (v0.4.0) 🔬 **Goal:** Power-user features and extensibility 11. ✅ **contracts_generate** - API contract generation 12. ✅ **templates_customize** - Custom template support 13. ❌ ~~**project_bootstrap**~~ - REMOVED (orchestration is AI's job, not tool's) 14. ✅ **metrics_report** - SDD analytics 15. ✅ **spec_lint** - Automated spec quality checking **Estimated Effort:** 4 stories × 4-6 tasks each = ~20 tasks (~1.5 sprints) **Updated:** Removed project_bootstrap (Spec Kit doesn't orchestrate) --- ### Phase 4: Polish & Optimization (v1.0.0) 💎 **Goal:** Production-ready, battle-tested 16. ✅ Cross-agent validation (Story 17) 17. ✅ Observability & diagnostics (Story 18) 18. ✅ Security hardening (Story 19) 19. ✅ Documentation polish (Story 20) 20. ✅ Performance optimization 21. ✅ Error handling improvements 22. ✅ Template library expansion **Estimated Effort:** 6 stories × 3-5 tasks each = ~25 tasks (~1.5 sprints) --- ## MCP-Specific Design Principles When implementing new features, follow these principles: ### ✅ DO: Enhance AI Coding Workflow - Tools that help AI understand codebase - Tools that generate artifacts AI can read - Tools that validate AI's work - Tools that reduce repetitive tasks ### ✅ DO: Stay Stateless & Composable - Each tool should work independently - Tools should compose naturally (output of one → input of another) - No server-side session state (use filesystem) ### ✅ DO: Leverage LLM Strengths - Generate structured markdown (AI reads well) - Create visual diagrams (Mermaid) - Summarize and analyze text - Suggest next actions ### ❌ DON'T: Replicate IDE Features - Don't build syntax highlighting - Don't create UI panels - Don't handle keybindings ### ❌ DON'T: Build Collaboration Infra - Don't implement team assignment - Don't build notification systems - Don't create user management ### ❌ DON'T: Execute Implementation - Don't write production code (AI's job) - Don't run build pipelines (quality tools OK) - Don't deploy (orchestration outside MCP scope) --- ## Feature Voting & Community Input We welcome community input on prioritization! To vote or suggest features: 1. **Vote on Existing Features:** [Open an issue](https://github.com/flight505/mcp-dincoder/issues) with title "Feature Vote: [Feature Name]" and add 👍 reaction 2. **Propose New Features:** [Start a discussion](https://github.com/flight505/mcp-dincoder/discussions) with use case and MCP fit analysis 3. **Champion Features:** Willing to implement? Comment "I can help with this" on feature issues ### Current Vote Leaders (To Be Tracked) - [ ] constitution_create - [ ] clarify_add/resolve - [ ] spec_validate - [ ] tasks_visualize - [ ] diagram_generate --- ## Success Metrics How will we know DinCoder is a "fully-fledged Spec Kit MCP server"? ### Quantitative Metrics - ✅ **Tool Coverage:** 20+ tools (currently 14) - ✅ **Spec Kit Parity:** 90%+ of core commands covered - ✅ **Quality Gates:** spec_validate, prereqs_check implemented - ✅ **Workflow Completeness:** Can execute full SDD cycle without leaving MCP ### Qualitative Metrics - ✅ **User Testimonials:** "I can build projects start-to-finish with DinCoder" - ✅ **AI Agent Adoption:** Works seamlessly with Claude Code, Cursor, Copilot - ✅ **Template Flexibility:** Users can customize for their workflows - ✅ **Clear Documentation:** New users can onboard in <10 minutes --- ## Next Steps 1. **Review This Analysis** - Get feedback on priorities and MCP fit 2. **Refine Roadmap** - Adjust phases based on community input 3. **Update plan.md** - Create detailed stories for Phase 1 features 4. **Prototype constitution_create** - Validate design with real implementation 5. **Gather Community Votes** - Which features do users want most? --- **Document Owner:** DinCoder Maintainers **Last Updated:** 2025-10-04 **Status:** Draft - Awaiting Review