Power your agent
คำสั่งของผู้เผยแพร่เอง ไม่ใช่เอนด์พอยต์ของ PlugMCP
Host a copy with our keys and logs.
uvx workflow-generator-mcp==0.5.0Claude Code ไม่มีลิงก์ติดตั้งสำหรับคำสั่งแบบโลคัล ให้คัดลอกคำสั่งด้านล่างแล้วรันในเทอร์มินัล
claude mcp add io.github.askuma-workflow-generator -- uvx workflow-generator-mcp==0.5.0รันในเทอร์มินัล
{ "mcpServers": { "io.github.askuma-workflow-generator": { "command": "uvx", "args": [ "workflow-generator-mcp==0.5.0" ] } }}claude_desktop_config.json
URL แบบรีโมตหรือคำสั่งบนเครื่องของคุณ? การติดตั้งสองแบบต่างกันอย่างไร
รันบน PlugMCP
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.
เปิดจากผู้เผยแพร่
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" ] } }}เครื่องมือ
ยังไม่มีรายการเครื่องมือ เซิร์ฟเวอร์แบบรีโมตจะได้รายการหลังการตรวจสอบสถานะครั้งแรก
FAQ
คัดลอกคอนฟิก stdio สำหรับ Cursor, Claude Desktop, Claude Code หรือ VS Code ได้ในหน้านี้ คอนฟิกนี้ใช้การติดตั้งของผู้เผยแพร่ ไม่ใช่พร็อกซีของ PlugMCP
ระเบียนนี้ไม่ได้ระบุตัวแปรสภาพแวดล้อมที่จำเป็น
Workflow Generator ทำงานแบบโลคัลผ่าน stdio
ตรวจสอบล่าสุดเมื่อ 2026-11-09 Architecture diagram with concurrency capacity and bottleneck estimates from any codebase.
