
Frontend Design Loop MCP
io.github.alexalexalex222由 GitHub 帳號 alexalexalex222 發布v1.0.0更新於 Oct 25, 2026
Design-first MCP for coding agents that improves websites with screenshot-grounded iteration.
概覽
一個本機 MCP 伺服器,讓編碼代理透過以渲染截圖為基礎的迭代改善現有前端,並回傳勝出修補與證明產物
- 可以做什麼
- 對儲存庫與設計目標執行主要設計流程:啟動本機預覽、擷取螢幕截圖、針對實際渲染頁面迭代,並回傳勝出修補、截圖與執行產物。在隔離工作樹中評估既有修補,執行確定性檢查、截圖並回傳證明欄位。使用較精簡的工具包變體取得操作手冊、建構脈絡、執行閘門、啟動與停止預覽並截圖,由主機代理自行評判。執行進階無人值守求解流程。
- 適用情境
- 適用於頁面已能運作但外觀平庸、扁平或設計不足,希望代理真正改善設計而不只是評論的情況。也適合已有修補、希望在採納前取得確定性檢查與截圖證明的場景。
- 執行需求
- 從 PyPI 以 pipx 本機安裝 Python 套件,並提供安裝輔助程式註冊偵測到的用戶端。本機安裝路徑會安裝 Playwright Chromium。每次呼叫需提供本機預覽指令與 URL,並為生成與視覺選擇 provider 與模型通道(例如 Gemini CLI 通道)。
安裝
在 SourceWeft 中
- 開啟 儀表板中的 Frontend Design Loop MCP,將其新增到工作區。
- 為需要使用其工具的對話啟用該服務。
Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。
其他 MCP 客戶端
參照 儲存庫 中的啟動說明。
README
Frontend Design Loop MCP
Coding agents can get a page functional. Frontend Design Loop makes it materially better with screenshot-grounded iteration and proof artifacts.
Use it when the base model got the page working but the result is still generic, flat, rough, or visibly under-designed. The main design workflow stays on one main provider and model lane by default, so multi-model routing is opt-in instead of the default story.
Quick Start
Install the current public build from PyPI:
Set up every detected supported client:
Real MCP call example:
Agent-Owned Toolkit Variant
This repo now also ships an additive MCP entrypoint for agent-owned frontend loops:
- console script:
frontend-design-toolkit-mcp - module fallback:
PYTHONPATH=src .venv/bin/python -m design_toolkit.server
That variant is intentionally narrow:
get_playbookbuild_contextrun_gatespreview_startcapture_screenshotspreview_stop
It does not hide vision scoring, creativity judging, or patch generation behind MCP. The host agent owns planning, edits, screenshot review, scoring, iteration, and winner selection directly.
See test-prompt-codex.md for the end-to-end Codex exercise prompt.
What The MCP Does
frontend_design_loop_design is the main workflow:
- the host agent points the MCP at a real repo plus a concrete design goal
- the MCP boots a local preview, captures screenshots, and iterates against the rendered result
- the same main provider and model lane is used by default across planning, generation, and vision unless you explicitly override it
- the MCP returns the winning patch plus screenshots and run artifacts
frontend_design_loop_eval is the proof workflow:
- use it when the host agent already has the patch
- the MCP applies the patch in an isolated worktree, runs deterministic checks, captures screenshots, and returns proof artifacts
This is the wedge:
- coding agents can already get pages working
- this MCP helps them make pages materially better
- screenshot-grounded iteration plus proof artifacts is the differentiator
Official MCP Registry metadata is tracked in server.json.
Proof Gallery
The public proof set uses owned/generated GA SMB previews plus the ACA full-page before/after.
Selected Hero / Top Crops
ACA Full-Page Before / After
Before: early ACA full homepage.
After: rebuilt ACA homepage with a stronger hero, cleaner sequencing, and a materially better full-page result.
See the proof notes in the case studies index.
How It Works In Practice
- Point the MCP at a real repo and give it a concrete design goal.
- It creates an isolated worktree, boots a preview, and captures rendered screenshots.
- It iterates against the actual rendered page instead of only raw code.
- It returns the winning patch, screenshot proof, and run artifacts so the host agent can judge the result.
Workflow Summary
frontend_design_loop_design
Use it when:
- the page is functional but weak
- the section structure is there but the design is not
- you want the MCP to improve the page instead of only judging it
Key defaults:
- one main
provider+modellane by default planning_mode="single"vision_mode="on"section_creativity_mode="on"- split planner or vision lanes only happen when explicitly requested
frontend_design_loop_eval
Use it when:
- the host agent already has the patch
- you want deterministic checks, screenshots, and artifact capture
- you want the host agent to judge the result from returned screenshots
Returned proof fields include:
deterministic_passedvision_pendingvision_scoredfinal_passrun_dircandidate_dirscreenshot_filespatch
frontend_design_loop_solve
frontend_design_loop_solve still exists for advanced unattended workflows, but it is not the main public story.
Install And Setup
Public install now
GitHub install remains the fallback:
Local clone path
The local setup path:
- creates
.venv - installs the package
- installs Playwright Chromium
- installs detected client entries when supported clients are present
- runs the built-in doctor
- runs the stdio smoke test
If you want the repo-local environment without auto-installing client entries:
Setup helpers
Bulk installer:
Targeted installers:
Config printers:
Safety Defaults
- custom commands are parsed as shell-free argv by default
- shell syntax, substitutions, and inline interpreter execution like
bash -c,python -c, andnode -erequireunsafe_shell_commands=true preview_urlmust match the launched local preview origin and port by default- external preview fetches require
unsafe_external_preview=true - preview readiness checks reject cross-origin redirects, and browser screenshots block cross-origin subresources by default
- auto-context skips common secret-bearing paths such as
.env*,.git/,.aws/,.ssh/,.config/gcloud/,.docker/,.kube/, token-named files, and service-account-style JSON - native CLI providers inherit a minimal allowlisted environment instead of the full host shell environment
- shared worktree reuse directories are off by default
Client-side vision is the default proof path for frontend_design_loop_eval, so the host agent can judge the screenshots without provider credentials.
Proxy-only MiniMax vision lanes are explicitly treated as structural-only review:
vision_review_mode="proxy_structural"- they do not count as full automated visual scoring
Verification
Offline preflight:
stdio smoke:
Built-in doctor:
Docs
Distribution State
Current public install path:
來源:README.md,提交 c65f7ca
工具
0版本歷史
1- v1.0.0最新Sep 16, 2026
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