에이전트 강화
게시자의 자체 명령입니다. PlugMCP 엔드포인트가 아닙니다.
Host a copy with our keys and logs.
uvx workflow-generator-mcp==0.5.0Claude Code에는 로컬 명령용 설치 링크가 없습니다. 아래 명령을 복사해 터미널에서 실행합니다.
claude mcp add io.github.askuma-workflow-generator -- uvx workflow-generator-mcp==0.5.0터미널에서 실행합니다.
{ "mcpServers": { "io.github.askuma-workflow-generator": { "command": "uvx", "args": [ "workflow-generator-mcp==0.5.0" ] } }}claude_desktop_config.json
원격 URL인가요, 내 컴퓨터에서 실행하는 명령인가요? 두 설치 방식의 차이
PlugMCP에서 실행
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
실행게시자 제공
Scan any project and generate WORKFLOW.html — a dark-mode visual system diagram showing every component, how they talk to each other, and where your throughput ceiling actually is.
Works with Python, Node.js, Go, Java, Rust, Ruby, and mixed projects. No external dependencies for the core scanner.
Vendored and generated directories (node_modules, venv, , , …) are never scanned,
and capacity figures are clearly labeled as static-analysis estimates.
Architecture diagram with concurrency capacity and bottleneck estimates from any codebase.
site-packagesdistLive demo → — generated from fastapi/full-stack-fastapi-template, unmodified.
(real CLI output, unscripted — static screenshot if you'd rather not autoplay)
Every generated page contains:
Every source file (Python, JS/TS, Go, Java, Rust, Ruby) becomes a node; every real import becomes
an edge — resolved with a language-appropriate parser (Python's ast module, regex for JS/TS/Go/
Java/Rust/Ruby), not guessed. Files that match an already-detected component (an LLM call, a
database client, a queue) get an edge to that component too, so you can see exactly which files
talk to Redis, OpenAI, etc. Large repos (350+ files) are automatically aggregated into
directory-level nodes so the graph stays readable; override with --graph-detail files or
--graph-detail dirs.
By default the graph only shows what the code says (import direction, static "this file calls
Redis"), which is honest but not the same as real traffic. Pass --access-log /path/to/access.log
(any combined/common log format) to overlay real observed request counts onto the HTTP-entry
edges — and the generated report includes a ready-to-run k6 load-test script
covering up to 5 detected routes, so the "Practical throughput" number can be checked against a
real measurement instead of only a static-analysis estimate.
pip install workflow-generator-mcp
workflow-generator . WORKFLOW.html # CLI: scan and write the report
workflow-generator-mcp # stdio MCP server
With pip installed, any MCP host config reduces to:
{
"mcpServers": {
"workflow-generator": { "command": "workflow-generator-mcp" }
}
}
mkdir -p ~/.claude/skills
git clone https://github.com/askuma/workflow-generator.git ~/.claude/skills/workflow-generator
Then in any Claude Code session:
/workflow-generator
/workflow-generator /path/to/project
1. Install the dependency:
pip install mcp
2. Add to your MCP host config (replace ~ with your actual home path):
~/Library/Application Support/Claude/claude_desktop_config.json (Mac)
%APPDATA%\Claude\claude_desktop_config.json (Windows)
{
"mcpServers": {
"workflow-generator": {
"command": "python3",
"args": ["~/.claude/skills/workflow-generator/mcp/server.py"]
}
}
}
.vscode/mcp.json
{
"servers": {
"workflow-generator": {
"type": "stdio",
"command": "python3",
"args": ["~/.claude/skills/workflow-generator/mcp/server.py"]
}
}
}
~/.cursor/mcp.json
{
"mcpServers": {
"workflow-generator": {
"command": "python3",
"args": ["~/.claude/skills/workflow-generator/mcp/server.py"]
}
}
}
.zed/settings.json
{
"context_servers": {
"workflow-generator": {
"command": {
"path": "python3",
"args": ["~/.claude/skills/workflow-generator/mcp/server.py"]
}
}
}
}
~/.windsurf/mcp_config.json
{
"mcpServers": {
"workflow-generator": {
"command": "python3",
"args": ["~/.claude/skills/workflow-generator/mcp/server.py"]
}
}
}
3. Restart your tool, then ask:
generate a workflow diagram for this project
how many concurrent requests can this handle?
show me the system architecture
MCP tools exposed:
generate_workflow — scans project, writes WORKFLOW.html, optionally opens in browseranalyze_workflow — returns structured JSON summary (no file written)No install needed beyond Python 3.8+:
python3 ~/.claude/skills/workflow-generator/scripts/analyze.py . ~/WORKFLOW.html
# then open ~/WORKFLOW.html
Optional flags:
--access-log /path/to/access.log # overlay real request counts onto the dependency graph
--graph-detail auto|files|dirs # force file-level or directory-level graph nodes (default: auto)
Written: /your/project/WORKFLOW.html
Framework: FastAPI · Workers: 8 · Concurrent I/O: ~800
Practical throughput: ~50–200 req/min
Bottleneck: OpenAI (LLM latency 3–30s per call)
Gateway: nginx · 2 rate limit zone(s)
LLM: OpenAI · eval: TruLens RAG Triad
Storage: Qdrant, Redis
External sources: Jira, Azure DevOps, Slack
workflow-generator/
├── SKILL.md ← Claude Code skill definition
├── INSTALL.md ← detailed per-platform install guide
├── workflow_generator_mcp/
│ ├── analyze.py ← core scanner + HTML renderer (stdlib only)
│ └── server.py ← MCP stdio server (package form)
├── scripts/
│ └── analyze.py ← thin compatibility shim -> workflow_generator_mcp/analyze.py
├── tests/ ← pytest suite for the scanner
├── mcp/
│ ├── server.py ← MCP stdio server
│ └── requirements.txt ← pip install mcp
└── copilot/
├── index.js ← GitHub Copilot Extension (Express)
├── package.json
└── openai_function.json
MIT
Architecture diagram with concurrency capacity and bottleneck estimates from any codebase.
{ "mcpServers": { "io.github.askuma-workflow-generator": { "command": "uvx", "args": [ "workflow-generator-mcp==0.5.0" ] } }}도구
아직 도구 목록이 없습니다. 원격 서버는 첫 상태 확인 후에 도구 목록이 생깁니다.
FAQ
이 페이지에서 Cursor, Claude Desktop, Claude Code 또는 VS Code용 stdio 설정을 복사합니다. PlugMCP 프록시가 아니라 게시자의 설치 방식을 사용합니다.
이 레코드에는 필수 환경 변수가 등록되어 있지 않습니다.
Workflow Generator은(는) stdio를 통해 로컬에서 실행됩니다.
마지막 확인: 2026-11-09. Architecture diagram with concurrency capacity and bottleneck estimates from any codebase.
