Provides browser automation tools for monitoring console logs and inspecting network requests, with support for Chrome, Edge, and Brave browsers.
Applies Google Gemini API prompting strategies including few-shot examples, output formatting, context optimization, and prompt decomposition for enhanced prompt engineering.
Supports code analysis and quality evaluation for JavaScript projects including symbol search, reference tracking, and complexity assessment.
Provides AST-based code analysis for Python projects including symbol search, reference tracking, complexity calculation, and quality metrics evaluation.
Uses SQLite for intelligent memory management with features including context compression, session restoration, transaction support, and indexing for long-term information storage.
Offers semantic code analysis and quality evaluation for TypeScript projects with AST-based symbol search, reference tracking, complexity metrics, and coupling/cohesion analysis.
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
Smithery Platform
MCP Client Configuration
Add to your Claude Desktop or other MCP client's configuration file:
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
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
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(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:
v1.6.0 Response Example:
Development Guide
Environment Setup
Testing
Code 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:
Star History
Hi-AI v1.6.0
Tasks Support · Cursor-Based Pagination · 36 Specialized Tools · 122 Tests · 100% Coverage
Made with ❤️ by Su