Hi-AI
SSD-AI
AI Development Assistant based on Model Context Protocol
TypeScript + Python Support · 36 Specialized Tools · Intelligent Memory Management · Code Analysis · Reasoning Framework · Tasks Support
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 memoryrecall_memory- Search stored informationauto_save_context- Automatic context savingrestore_session_context- Session restorationprioritize_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 definitionsfind_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 analysisvalidate_code_quality- Code quality evaluationcheck_coupling_cohesion- Coupling/cohesion analysissuggest_improvements- Improvement suggestionsapply_quality_rules- Quality rule applicationget_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 generationcreate_user_stories- User story creationanalyze_requirements- Requirements analysisfeature_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 creationanalyze_problem- Problem analysisstep_by_step_analysis- Step-by-step analysisbreak_down_problem- Problem decompositionthink_aloud_process- Thinking process expressionformat_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 enhancementanalyze_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 monitoringinspect_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 statustasks/result- Query task result (wait until completion)tasks/list- List all tasks (with pagination)tasks/cancel- Cancel running tasknotifications/tasks/status- Status change notifications
Task-Enabled Tools (11 tools)
Semantic Analysis:
find_symbol,find_referencesCode Quality:
analyze_complexity,check_coupling_cohesion,validate_code_quality,suggest_improvementsProject Planning:
analyze_requirements,feature_roadmap,generate_prdReasoning/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 listprompts/list- Prompt listtasks/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-aiSmithery Platform
# One-click installation
https://smithery.ai/server/@su-record/hi-aiMCP 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_referencesCode Quality:
analyze_complexity,check_coupling_cohesion,validate_code_quality,suggest_improvementsProject Planning:
analyze_requirements,feature_roadmap,generate_prdReasoning/Prompts:
apply_reasoning_framework,enhance_prompt_gemini
Keyword Mapping Examples
Memory Tools
Tool | English | Korean |
save_memory | remember, save this | 기억해, 저장해 |
recall_memory | recall, remind me | 떠올려, 기억나 |
auto_save_context | commit, checkpoint | 커밋, 저장 |
Code Analysis Tools
Tool | English | Korean |
find_symbol | find function, where is | 함수 찾아, 클래스 어디 |
analyze_complexity | complexity, how complex | 복잡도, 복잡한지 |
validate_code_quality | quality, review | 품질, 리뷰 |
Tasks Tools
Tool | English | Korean |
tasks/get | task status, progress | 작업 상태, 진행 상황 |
tasks/result | get result, wait for completion | 결과 가져와, 완료될 때까지 |
tasks/cancel | cancel task, stop | 작업 취소, 중지해 |
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 --> C5Core 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)
↓
Keyword Matching (Tool Selection)
↓
Tasks Support Check
↓
Normal Execution or Task Creation
↓
Asynchronous Execution (Tasks)
↓
Status Polling or Real-time Notifications
↓
Result ReturnPerformance
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(n²) → 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:
✓ Saved: test-key
Category: generalDevelopment 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 devTesting
# Run all tests
npm test
# Watch mode
npm run test:watch
# UI mode
npm run test:ui
# Coverage report
npm run test:coverageCode Style
TypeScript: strict mode
Types: Use
src/types/tool.tsTests: Maintain 100% coverage
Commits: Conventional Commits format
Adding New Tools
Create file in
src/tools/category/directoryImplement
ToolDefinitioninterfaceRegister tool in
src/index.tsWrite tests in
tests/unit/directoryUpdate README
Pull Request
Create feature branch:
feature/tool-nameWrite 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}
}Star History
Hi-AI v1.6.0
Tasks Support · Cursor-Based Pagination · 36 Specialized Tools · 122 Tests · 100% Coverage
Made with ❤️ by Su
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