SSD-AI
Table of Contents
Overview
Hi-AI is an AI development assistant that implements the Model Context Protocol (MCP) standard. It provides 36 specialized tools through natural language keyword recognition, helping developers perform complex tasks intuitively.
Core Values
- Natural Language: Execute tools automatically through Korean/English keywords
- Intelligent Memory: Context management and compression using SQLite
- Multi-Language Support: TypeScript, JavaScript, Python code analysis
- Performance Optimization: Project caching system
- Enterprise Quality: 100% test coverage and strict type system
- Long-Running Support: Task management for asynchronous operations
- Large-Scale Data: Cursor-based pagination
Key Features
1. Memory Management System
10 tools for maintaining context across sessions:
- Intelligent Storage: Information classification and priority management by category
- Context Compression: Priority-based context compression system
- Session Restoration: Perfect recreation of previous work states
- SQLite-Based: Concurrent control, indexing, transaction support
Key Tools:
save_memory - Store information in long-term memory
recall_memory - Search stored information
auto_save_context - Automatic context saving
restore_session_context - Session restoration
prioritize_memory - Memory priority management
2. Semantic Code Analysis
AST-based code analysis and navigation tools:
- Symbol Search: Locate function, class, variable positions across projects
- Reference Tracking: Track all usages of specific symbols
- Multi-Language: TypeScript, JavaScript, Python support
- Project Caching: Performance optimization through LRU cache
Key Tools:
find_symbol - Search for symbol definitions
find_references - Find symbol references
3. Code Quality Analysis
Comprehensive code metrics and quality evaluation:
- Complexity Analysis: Cyclomatic, Cognitive, Halstead metrics
- Coupling/Cohesion: Structural soundness evaluation
- Quality Scores: A-F grade system
- Improvement Suggestions: Actionable refactoring recommendations
Key Tools:
analyze_complexity - Complexity metric analysis
validate_code_quality - Code quality evaluation
check_coupling_cohesion - Coupling/cohesion analysis
suggest_improvements - Improvement suggestions
apply_quality_rules - Quality rule application
get_coding_guide - Coding guide lookup
4. Project Planning Tools
Systematic requirements analysis and roadmap generation:
- PRD Generation: Automatic product requirements document creation
- User Stories: Story writing including acceptance criteria
- MoSCoW Analysis: Requirements prioritization
- Roadmap Creation: Step-by-step development schedule planning
Key Tools:
generate_prd - Product requirements document generation
create_user_stories - User story creation
analyze_requirements - Requirements analysis
feature_roadmap - Feature roadmap creation
5. Sequential Thinking Tools
Structured problem solving and decision making support:
- Problem Decomposition: Break down complex problems step by step
- Thinking Chains: Sequential reasoning process generation
- Multiple Perspectives: Analytical/Creative/Systematic/Critical thinking
- Execution Plans: Convert tasks into executable plans
Key Tools:
create_thinking_chain - Thinking chain creation
analyze_problem - Problem analysis
step_by_step_analysis - Step-by-step analysis
break_down_problem - Problem decomposition
think_aloud_process - Thinking process expression
format_as_plan - Plan formatting
6. Prompt Engineering
Prompt quality improvement and optimization:
- Automatic Enhancement: Convert vague requests to specific ones
- Quality Evaluation: Score clarity, specificity, contextuality
- Structuring: Goal, background, requirements, quality criteria
Key Tools:
enhance_prompt - Prompt enhancement
analyze_prompt - Prompt quality analysis
7. Browser Automation
Web-based debugging and testing:
- Console Monitoring: Browser console log capture
- Network Analysis: HTTP request/response tracking
- Cross-Platform: Chrome, Edge, Brave support
Key Tools:
monitor_console_logs - Console log monitoring
inspect_network_requests - Network request analysis
8. UI Preview
Pre-coding UI layout visualization:
- ASCII Art: Support for 6 layout types
- Responsive Preview: Desktop/mobile views
- Pre-Approval: Confirm structure before coding
Key Tools:
preview_ui_ascii - ASCII UI preview
9. Time Utilities
Various format time queries:
Key Tools:
get_current_time - Current time query (ISO, UTC, timezones, etc.)
10. Tasks and Pagination Support
Long-running operations and large-scale data processing:
- Tasks: MCP 2025-11-25 experimental feature for long-running task management
- Pagination: Cursor-based pagination for large dataset processing
- Asynchronous Operations: Execute complex analysis tasks in background
- Status Tracking: Real-time task progress monitoring
Tasks-Enabled Tools:
find_symbol, find_references (semantic analysis)
analyze_complexity, check_coupling_cohesion, validate_code_quality, suggest_improvements (code quality)
analyze_requirements, feature_roadmap, generate_prd (project planning)
apply_reasoning_framework, enhance_prompt_gemini (reasoning and prompts)
v1.6.0 Update
New Features (2025-01-27)
Tasks Support (Experimental MCP Feature)
Long-Running Task Management
- Implementation of MCP 2025-11-25 Tasks specification
- Execute complex analysis tasks in background
- Real-time task status tracking and monitoring
- TTL-based automatic cleanup (default 5 minutes, max 1 hour)
Tasks API
tasks/get - Query task status
tasks/result - Query task result (wait until completion)
tasks/list - List all tasks (with pagination)
tasks/cancel - Cancel running task
notifications/tasks/status - Status change notifications
Task-Enabled Tools (11 tools)
- Semantic Analysis:
find_symbol, find_references
- Code Quality:
analyze_complexity, check_coupling_cohesion, validate_code_quality, suggest_improvements
- Project Planning:
analyze_requirements, feature_roadmap, generate_prd
- Reasoning/Prompts:
apply_reasoning_framework, enhance_prompt_gemini
Pagination Support
Cursor-Based Pagination
- MCP specification compliant cursor-based implementation
- Efficient processing of large lists
- Enhanced security through opaque cursors
Supported List Operations
tools/list - Tool list (20 items by default)
resources/list - Resource list
prompts/list - Prompt list
tasks/list - Task list
Integration Effects
- Asynchronous Operation Support: Execute complex analysis in background
- Large-Scale Data Processing: Improved memory efficiency through pagination
- Real-Time Monitoring: Task progress tracking
- Enhanced User Experience: Perform other tasks during long operations
Installation
System Requirements
- Node.js 18.0 or higher
- TypeScript 5.0 or higher
- MCP-compatible client (Claude Desktop, Cursor, Windsurf)
- Python 3.x (for Python code analysis)
Installation Methods
NPM Package
# Global installation
npm install -g @ssdeanx/ssd-ai
# Local installation
npm install @ssdeanx/ssd-ai
Smithery Platform
# One-click installation
https://smithery.ai/server/@su-record/hi-ai
MCP Client Configuration
Add to your Claude Desktop or other MCP client's configuration file:
{
"mcpServers": {
"hi-ai": {
"command": "hi-ai",
"args": [],
"env": {}
}
}
}
Tool Catalog
Complete Tool List (36 tools)
| Category | Count | Tool List |
|---|
| Memory | 10 | save_memory, recall_memory, list_memories, search_memories, delete_memory, update_memory, auto_save_context, restore_session_context, prioritize_memory, start_session |
| Semantic | 2 | find_symbol, find_references |
| Thinking | 6 | create_thinking_chain, analyze_problem, step_by_step_analysis, break_down_problem, think_aloud_process, format_as_plan |
| Reasoning | 1 | apply_reasoning_framework |
| Code Quality | 6 | analyze_complexity, validate_code_quality, check_coupling_cohesion, suggest_improvements, apply_quality_rules, get_coding_guide |
| Planning | 4 | generate_prd, create_user_stories, analyze_requirements, feature_roadmap |
| Prompt | 2 | enhance_prompt, analyze_prompt |
| Browser | 2 | monitor_console_logs, inspect_network_requests |
| UI | 1 | preview_ui_ascii |
| Time | 1 | get_current_time |
Tasks-Enabled Tools (11 tools)
The following tools support long-running operations through Tasks:
- Semantic Analysis:
find_symbol, find_references
- Code Quality:
analyze_complexity, check_coupling_cohesion, validate_code_quality, suggest_improvements
- Project Planning:
analyze_requirements, feature_roadmap, generate_prd
- Reasoning/Prompts:
apply_reasoning_framework, enhance_prompt_gemini
Keyword Mapping Examples
Memory Tools
| Tool | English | Korean |
|---|
| save_memory | remember, save this | ๊ธฐ์ตํด, ์ ์ฅํด |
| recall_memory | recall, remind me | ๋ ์ฌ๋ ค, ๊ธฐ์ต๋ |
| auto_save_context | commit, checkpoint | ์ปค๋ฐ, ์ ์ฅ |
Code Analysis Tools
| Tool | English | Korean |
|---|
| find_symbol | find function, where is | ํจ์ ์ฐพ์, ํด๋์ค ์ด๋ |
| analyze_complexity | complexity, how complex | ๋ณต์ก๋, ๋ณต์กํ์ง |
| validate_code_quality | quality, review | ํ์ง, ๋ฆฌ๋ทฐ |
Tasks Tools
| Tool | English | Korean |
|---|
| tasks/get | task status, progress | ์์
์ํ, ์งํ ์ํฉ |
| tasks/result | get result, wait for completion | ๊ฒฐ๊ณผ ๊ฐ์ ธ์, ์๋ฃ๋ ๋๊น์ง |
| tasks/cancel | cancel task, stop | ์์
์ทจ์, ์ค์งํด |
Architecture
System Structure
graph TB
subgraph "Client Layer"
A[Claude Desktop / Cursor / Windsurf]
end
subgraph "MCP Server"
B[Hi-AI v1.6.0]
end
subgraph "Core Libraries"
C1[MemoryManager]
C2[ContextCompressor]
C3[ProjectCache]
C4[PythonParser]
C5[TaskManager]
end
subgraph "Tool Categories"
D1[Memory Tools x10]
D2[Semantic Tools x2]
D3[Thinking Tools x6]
D4[Quality Tools x6]
D5[Planning Tools x4]
D6[Prompt Tools x2]
D7[Browser Tools x2]
D8[UI Tools x1]
D9[Time Tools x1]
D10[Tasks Support]
end
subgraph "Data Layer"
E1[(SQLite Database)]
E2[Project Files]
E3[Task Store]
end
A <--> B
B --> C1 & C2 & C3 & C4 & C5
B --> D1 & D2 & D3 & D4 & D5 & D6 & D7 & D8 & D9 & D10
C1 --> E1
C3 --> E2
C4 --> E2
C5 --> E3
D1 --> C1 & C2
D2 --> C3 & C4
D4 --> C4
D10 --> C5
Core Components
TaskManager
- Role: Lifecycle management of long-running tasks
- Features: Task creation, status tracking, result storage, TTL management
- States: working, input_required, completed, failed, cancelled
- Notifications: Real-time status change notifications
Pagination System
- Role: Efficient processing of large list data
- Method: Cursor-based pagination
- Security: Prevent data exposure through opaque cursors
Data Flow
User Input (Natural Language)
โ
Keyword Matching (Tool Selection)
โ
Tasks Support Check
โ
Normal Execution or Task Creation
โ
Asynchronous Execution (Tasks)
โ
Status Polling or Real-time Notifications
โ
Result Return
Performance
Major Optimizations
Project Caching
- Performance improvement for repeated analysis through LRU cache
- Maintain latest state with 5-minute TTL
- Resource management through memory limits
Memory Operations
- Batch operation optimization through SQLite transactions
- Time complexity improvement: O(nยฒ) โ O(n)
- Fast lookup through indexing
Tasks Optimization
- Improved UI responsiveness through background execution
- Prevent memory leaks through TTL-based automatic cleanup
- Efficient monitoring through status-based polling
Response Format
- Switch to concise response format
- Output focused on core information
v1.5.0 Response Example:
{
"action": "save_memory",
"key": "test-key",
"value": "test-value",
"category": "general",
"timestamp": "2025-01-16T12:34:56.789Z",
"status": "success",
"metadata": { ... }
}
v1.6.0 Response Example:
โ Saved: test-key
Category: general
Development Guide
Environment Setup
# Clone repository
git clone https://github.com/ssdeanx/ssd-ai.git
cd ssd-ai
# Install dependencies
npm install
# Build
npm run build
# Development mode
npm run dev
Testing
# Run all tests
npm test
# Watch mode
npm run test:watch
# UI mode
npm run test:ui
# Coverage report
npm run test:coverage
Code Style
- TypeScript: strict mode
- Types: Use
src/types/tool.ts
- Tests: Maintain 100% coverage
- Commits: Conventional Commits format
Adding New Tools
- Create file in
src/tools/category/ directory
- Implement
ToolDefinition interface
- Register tool in
src/index.ts
- Write tests in
tests/unit/ directory
- Update README
Pull Request
- Create feature branch:
feature/tool-name
- Write and pass tests
- Confirm successful build
- Create PR and request review
Contributors
Special Thanks
- Smithery - MCP server deployment and one-click installation platform
License
MIT License - Free to use, modify, and distribute
Citation
If you use this project for research or commercial purposes:
@software{hi-ai2024,
author = {ssdeanx},
title = {Hi-AI: Natural Language MCP Server for AI-Assisted Development},
year = {2024},
version = {1.6.0},
url = {https://github.com/su-record/hi-ai}
}