wassden
A powerful MCP-based Spec-Driven Development (SDD) toolkit that transforms any LLM into a comprehensive development agent with structured specification generation and validation capabilities.
"Spec-first development made intelligent and systematic"
🎯 Core Philosophy
wassden is designed as an intelligent prompt orchestrator for SDD workflows. It doesn't generate documents itself—instead, it:
- 🧠 Provides expert-crafted prompts that guide AI agents to create high-quality specifications
- 🤝 Enables seamless agent collaboration through standardized SDD workflows
- ✅ Validates generated documents to ensure consistency and completeness
- 📋 Maintains project coherence across the entire development lifecycle
The tool acts as your SDD methodology expert, ensuring AI agents follow best practices while you focus on the creative aspects of development.
✨ Key Features
- 🎯 Spec-Driven Development: Complete Requirements → Design → Tasks → Code workflow
- 🔄 Intelligent Validation: Automated quality checks with actionable feedback
- 📊 Traceability Management: Full REQ↔DESIGN↔TASK mapping and impact analysis
- 🚀 Progressive Prompting: Step-by-step guidance for high-quality deliverables
- 🛠️ MCP Integration: Seamless integration with Claude Code and other MCP clients via FastMCP
- 🧪 Robust Testing: 118 comprehensive tests with automated MCP integration and consistently fast response times (<0.01ms avg)
🎪 Demonstrations
Demo videos showcasing the development workflow will be added to this section soon.
Detailed usage examples will be provided in the videos—please stay tuned!
🚀 Quick Start
Installation
# Via uv (recommended)
uv pip install wassden
# Via pip
pip install wassden
# Via git
git clone https://github.com/tokusumi/wassden-mcp
cd wassden-py
uv pip install -e .
MCP Integration
Claude Code Setup (Recommended)
- Install the package
- Add to Claude Code settingsEdit
~/.claude/settings.json:{
"mcpServers": {
"wassden": {
"command": "wassden",
"args": ["serve", "--server"],
"env": {}
}
}
}
- Restart Claude Code
- Complete application restart required
- wassden tools will appear in tool palette
- Verify connection
# Manual verification
wassden check_completeness --userInput "test project"
Alternative: Development Installation
git clone https://github.com/tokusumi/wassden-mcp
cd wassden-py
uv pip install -e .
Then use absolute path in settings:
{
"mcpServers": {
"wassden": {
"command": "python",
"args": ["-m", "wassden.server"],
"env": {}
}
}
}
Basic Usage
- Complete Project Analysis & Requirements Generation
wassden check_completeness --userInput "Your project description"
- If information is missing: Returns clarifying questions
- If information is sufficient: Provides structured prompts for the agent to generate EARS format requirements.md
- Agent-Driven Document CreationThe agent uses wassden's prompts to create specifications:
- Requirements: Agent generates
specs/requirements.md using wassden's EARS-formatted prompts - Design: Agent creates
specs/design.md following wassden's architectural guidance - Tasks: Agent produces
specs/tasks.md with wassden's WBS structure
- Quality Assurance Through Validationwassden validates the agent-generated documents:
wassden validate_requirements specs/requirements.md
wassden validate_design specs/design.md
wassden validate_tasks specs/tasks.md
These tools provide structured prompts for AI agents to create specifications
| Tool | Purpose | Input | Agent Output |
|---|
check_completeness | Analyze input & provide requirements prompt | User description | Questions or structured requirements prompt |
prompt_requirements | Generate specialized requirements prompt | Project details | EARS-formatted requirements prompt |
prompt_design | Generate design document prompt | Requirements path | Architectural design prompt |
prompt_tasks | Generate WBS tasks prompt | Design path | Task breakdown prompt |
prompt_code | Generate implementation prompt | All spec paths | Implementation guide prompt |
These tools validate agent-generated documents for quality and consistency
| Tool | Purpose | Input | Output |
|---|
validate_requirements | Validate requirements quality | Requirements path | Validation report |
validate_design | Validate design structure | Design path | Validation report |
validate_tasks | Validate task dependencies | Tasks path | Validation report |
These tools provide project insights and traceability
| Tool | Purpose | Input | Output |
|---|
analyze_changes | Impact analysis | Changed file | Change impact report |
get_traceability | Traceability report | Spec paths | Full traceability matrix |
📁 Project Structure
wassden-py/
├── wassden/
│ ├── handlers/ # Tool implementation handlers
│ │ ├── completeness.py
│ │ ├── requirements.py
│ │ ├── design.py
│ │ ├── tasks.py
│ │ ├── code_analysis.py
│ │ └── traceability.py
│ ├── lib/ # Core functionality
│ │ ├── validate.py
│ │ ├── traceability.py
│ │ ├── fs_utils.py
│ │ └── prompts.py
│ ├── tools/ # MCP tool definitions
│ │ └── definitions.py
│ ├── server.py # FastMCP server
│ └── cli.py # CLI interface
├── specs/ # Generated specifications
│ ├── requirements.md
│ ├── design.md
│ └── tasks.md
├── tests/ # Comprehensive test suite
│ ├── unit/
│ ├── integration/
│ └── e2e/
└── pyproject.toml # Project configuration
🧩 Language & Framework Support
- Primary: Python 3.12+
- MCP Framework: FastMCP for high-performance MCP server implementation
- CLI: Click for command-line interface
- Testing: pytest + pytest-asyncio with 118 comprehensive tests (100% passing)
- Performance: 198,406+ req/sec throughput, <0.01ms avg response time
- Code Quality: ruff + mypy for linting and type checking
- Standards: EARS format, WBS structure, Traceability matrices
🎯 Use Cases
For Development Teams
- Requirement Analysis: Systematic requirements gathering with gap analysis
- Design Documentation: Structured design with automatic traceability
- Project Planning: WBS generation with dependency management
- Quality Assurance: Built-in validation and feedback loops
For AI Agents
- Structured Prompting: Progressive prompts for complex development tasks
- Context Preservation: Maintains context across development phases
- Error Recovery: Validation-driven error detection and correction
- Change Management: Impact analysis for specification changes
For Technical Writers
- Documentation Generation: Automated prompt generation for technical documentation
- Consistency Checking: Format and structure validation
- Cross-reference Management: Automatic traceability link generation
🔧 Configuration
Default Paths
{
"requirementsPath": "specs/requirements.md",
"designPath": "specs/design.md",
"tasksPath": "specs/tasks.md"
}
Validation Rules
- EARS Format: Requirements must follow "システムは...すること" pattern
- REQ-ID Format: Sequential numbering (REQ-01, REQ-02, ...)
- Traceability: All design elements must reference requirements
- Dependencies: Task dependencies checked for circular references
wassden-py delivers exceptional performance for production AI agent deployments:
Response Times
- Average Response: <0.01ms ⚡️
- P95 Response: <0.01ms
- Min Response: <0.01ms
- Sub-millisecond: Consistent ultra-fast responses
Throughput & Scalability
- Peak Throughput: 198,406+ requests/second 🚀
- Concurrent Load: 50+ agents simultaneously
- Sustained Performance: 150,000+ req/sec under load
- Memory Efficiency: Minimal Python overhead
Reliability
- Test Coverage: 118 comprehensive tests (100% passing)
- Automated Integration: Full MCP server testing
- Error Recovery: 100% graceful error handling
- Memory Leaks: Zero detected (active optimization)
- Agent Compatibility: Claude Code, Cursor, VS Code verified
🧪 Development & Testing
# Install development dependencies
uv pip install -e ".[dev]"
# Quality checks (recommended)
make check # Run format, lint, typecheck, and test with coverage
make ci # CI checks without modifying files
# Run MCP server
wassden serve --server # Start MCP server
python -m wassden.server # Alternative method
🤝 Contributing
- Fork the repository
- Create your feature branch (
git checkout -b feature/amazing-feature) - Run quality checks (
make check) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
📄 License
MIT License – see LICENSE file for details.
🙏 Acknowledgments
- Model Context Protocol: Enabling seamless AI-tool integration
- FastMCP: High-performance MCP server framework for Python
- Claude Code: Primary development and testing environment
- Spec-Driven Development: Methodology inspiration and best practices
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