AI 어시스턴트에게 모든 코드베이스에 대한 깊은 이해력을 제공하세요
Gemini, Claude, Cursor, Windsurf, VS Code Copilot에 코드 분석 데이터를 제공하는 독립형 MCP 서버로, 대화 간 지속적인 AI 메모리를 지원합니다.
Your Code → CodeAtlas Analyze → MCP Server → AI Understands Everything
│
10 powerful tools:
• Search functions & classes
• Trace feature flows
• Generate architecture diagrams
• Persistent memory across conversationsCodeAtlas 이전: AI는 맹목적으로 grep을 수행하며, 대화가 바뀔 때마다 프로젝트를 잊어버립니다. CodeAtlas 이후: AI가 아키텍처와 의존성을 즉시 파악하고 컨텍스트를 기억합니다.
Related MCP server: code-intel MCP Server
CodeAtlas 확장 프로그램을 설치한 후 다음을 수행하세요:
Ctrl+Shift+P → CodeAtlas: Analyze Project{
"mcpServers": {
"codeatlas": {
"command": "npx",
"args": ["-y", "@giauphan/codeatlas-mcp"]
}
}
}{
"mcpServers": {
"codeatlas": {
"command": "npx",
"args": ["-y", "@giauphan/codeatlas-mcp"]
}
}
}{
"mcp": {
"servers": {
"codeatlas": {
"command": "npx",
"args": ["-y", "@giauphan/codeatlas-mcp"]
}
}
}
}{
"mcpServers": {
"codeatlas": {
"command": "npx",
"args": ["-y", "@giauphan/codeatlas-mcp"]
}
}
}claude mcp add codeatlas -- npx -y @giauphan/codeatlas-mcp{
"mcpServers": {
"codeatlas": {
"command": "npx",
"args": ["-y", "@giauphan/codeatlas-mcp"]
}
}
}완료! 이제 AI가 10가지 CodeAtlas 도구를 모두 사용할 수 있습니다.
도구 | 설명 | 사용 예시 |
| 분석된 모든 프로젝트 나열 | "내 프로젝트가 뭐야?" |
| 모듈, 클래스, 함수 가져오기 | "모든 클래스를 보여줘" |
| 가져오기/호출/포함 관계 | "UserService는 무엇에 의존해?" |
| 코드 품질 및 보안 분석 | "보안 문제가 있어?" |
| 이름으로 엔티티 퍼지 검색 | "로그인 함수 찾아줘" |
| 특정 파일의 모든 엔티티 | "auth.ts에 뭐가 들어있어?" |
도구 | 설명 | 사용 예시 |
| Mermaid 아키텍처 다이어그램 (모듈 임포트) | "시스템 아키텍처를 보여줘" |
| Mermaid 실행 흐름 다이어그램 (호출 체인) | "결제 기능은 어떻게 작동해?" |
도구 | 설명 | 사용 예시 |
| 컨텍스트를 | "우리가 변경한 내용을 기억해" |
| 코드베이스를 통한 기능 추적 | "auth에는 어떤 파일들이 관련돼?" |
AI 어시스턴트는 대화가 끝나면 모든 것을 잊어버립니다. CodeAtlas는 이를 다음과 같이 해결합니다:
Conversation 1 → AI analyzes code → sync_system_memory
│
.agents/memory/
├── system-map.md ← Architecture diagram
├── modules.json ← All entities
├── conventions.md ← Code patterns
├── business-rules.json ← Domain logic
├── feature-flows.json ← Feature traces
└── change-log.json ← Change history
│
Conversation 2 → AI reads memory → full context restored instantly ✨Analyze Project를 실행하면 CodeAtlas가 AI IDE를 위한 규칙 파일을 자동으로 생성합니다:
생성된 파일 | 대상 |
| 모든 AI 어시스턴트 |
| Cursor |
| Claude Code |
| Windsurf |
이 파일들은 AI에게 다음을 지시합니다:
모든 대화 시작 시 .agents/memory/를 읽을 것
변경 사항을 만들기 전에 MCP 도구를 사용할 것
변경 완료 후 sync_system_memory를 호출할 것
언어 | 기능 |
TypeScript / JavaScript | 전체 AST: 임포트, 클래스, 함수, 변수, 호출, 구현 |
Python | 클래스, 함수, 변수, 임포트, 호출 |
PHP | 클래스, 인터페이스, 트레이트, 열거형, 함수, 속성 |
Blade Templates |
|
npm install -g @giauphan/codeatlas-mcp그런 다음 MCP 설정에서 "command": "codeatlas-mcp"를 사용하세요 (args는 필요 없음).
변수 | 설명 |
| 특정 프로젝트 디렉토리 강제 지정 |
기본적으로 서버는
~/아래에서.codeatlas/analysis.json이 있는 모든 프로젝트를 자동으로 검색합니다.
git clone https://github.com/giauphan/codeatlas-mcp.git
cd codeatlas-mcp
npm install
npm run build
npm test # 6 tests
npm start # Start MCP serverCodeAtlas Extension — 대화형 코드 그래프를 제공하는 VS Code 확장 프로그램
MCP Protocol — Model Context Protocol 표준
MIT — 개인 및 상업적 용도로 무료 사용 가능.
Generate a Mermaid diagram showing the EXECUTION FLOW of a feature. Unlike generate_system_flow (which shows module imports), this traces the actual call chain: entry point → controller → service → model → database. Given a keyword, it finds all related functions and classes, then builds a flowchart or sequence diagram showing how they call each other at runtime. This is the best tool for understanding HOW a feature works step-by-step.
Inputs: keyword (required), project, diagramType, depth, +1 more.
Auto-generate a Mermaid flowchart diagram showing how modules, classes, and functions connect in the system. Returns a Mermaid diagram string that AI can read to understand the full system flow without reading every file.
Inputs: project, scope, feature, maxNodes.
Get import/call/containment/implements relationships between entities. Shows how modules, classes, and functions are connected.
Inputs: project, source, target, relationship, +1 more.
Get all entities (classes, functions, variables) defined in a specific file.
Inputs: filePath (required), project.
Get AI-generated code insights including refactoring suggestions, security issues, and maintainability analysis.
Inputs: no named parameters.
Get all modules, classes, functions, and variables in the analyzed project. Returns entity type, name, file path, and line number.
Inputs: project, type, limit.
List all projects that have been analyzed by CodeAtlas. Returns project names, paths, and last analysis time.
Inputs: no named parameters.
Search for functions, classes, modules, or variables by name. Supports fuzzy matching.
Inputs: query (required), project, type.
Create or update the .agents/memory/ folder with auto-generated system documentation. This folder serves as AI's 'long-term memory' — it persists between conversations. After calling this, AI in any future conversation can read these files to understand the full system flow without re-analyzing. Call this after completing any code changes.
Inputs: project, businessRule, changeDescription.
Trace the complete flow of a feature through the codebase. Given a keyword (e.g. 'login', 'payment', 'crawl'), finds all related files, classes, and functions, then orders them by dependency chain to show the execution flow. This helps AI understand which files to read when working on a feature.
Inputs: keyword (required), project, depth.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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Scored across 10 tools
Most tools have distinct purposes. However, `generate_feature_flow_diagram` and `trace_feature_flow` both trace feature flow by keyword, differing only in output format (diagram vs. ordered list). Descriptions help differentiate, but an agent might still select the wrong one for a given need.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., `generate_feature_flow_diagram`, `get_dependencies`, `list_projects`). No mixed conventions or vague verbs.
With 10 tools, the set covers the domain of code analysis and documentation well. Each tool serves a distinct, necessary function without redundancy or bloat.
The tool surface covers core CRUD-like operations for projects and entities, plus advanced analysis (flows, insights, memory sync). Minor gaps exist, such as no tool for editing entities or comparing versions, but core workflows are supported.
Search indexed code, trace dependencies, assess change impact, and recall repository memory.
The Cortex MCP server provides read-only access to real-time engineering context from the Cortex developer portal, allowing AI coding assistants to answer natural language questions about your organization's catalog (microservices, libraries, domains, teams, infrastructure), scorecards (engineering standards and best practices), initiatives (goals and deadlines), and Engineering Intelligence metrics. It includes tools for querying documentation, tracking personal entities, and accessing AI-assisted insights across the entire Cortex ecosystem.
Give your AI agent a persistent map of your project's structure, dependencies, and bugs.
Code intelligence for LLMs. Analyze, search, and retrieve code from any public git repository.
