Skip to main content
Glama
alexalexalex222

io.github.alexalexalex222/frontend-design-loop-mcp

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:

pipx install frontend-design-loop-mcp

Set up every detected supported client:

frontend-design-loop-setup --install-all-detected-clients

Real MCP call example:

frontend_design_loop_design(
  repo_path="/absolute/path/to/site",
  goal="make the homepage look materially more premium without changing the information architecture",
  provider="gemini_cli",
  model="gemini-3.1-pro-preview",
  preview_command="python3 -m http.server {port}",
  preview_url="http://127.0.0.1:{port}/index.html"
)

Related MCP server: vibe-annotations

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_playbook

  • build_context

  • run_gates

  • preview_start

  • capture_screenshots

  • preview_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.

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.

ACA full-page before

After: rebuilt ACA homepage with a stronger hero, cleaner sequencing, and a materially better full-page result.

ACA full-page after

See the proof notes in the case studies index.

How It Works In Practice

  1. Point the MCP at a real repo and give it a concrete design goal.

  2. It creates an isolated worktree, boots a preview, and captures rendered screenshots.

  3. It iterates against the actual rendered page instead of only raw code.

  4. 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 + model lane 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_passed

  • vision_pending

  • vision_scored

  • final_pass

  • run_dir

  • candidate_dir

  • screenshot_files

  • patch

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

pipx install frontend-design-loop-mcp
frontend-design-loop-setup --install-all-detected-clients

GitHub install remains the fallback:

pipx install git+https://github.com/alexalexalex222/frontend-design-loop-mcp.git
frontend-design-loop-setup --install-all-detected-clients

Local clone path

git clone https://github.com/alexalexalex222/frontend-design-loop-mcp.git
cd frontend-design-loop-mcp
./scripts/setup.sh

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:

FDL_SKIP_CLIENT_INSTALL=1 ./scripts/setup.sh

Setup helpers

Bulk installer:

frontend-design-loop-setup --install-all-detected-clients

Targeted installers:

frontend-design-loop-setup --install-claude --scope user
frontend-design-loop-setup --install-codex
frontend-design-loop-setup --install-gemini
frontend-design-loop-setup --install-droid
frontend-design-loop-setup --install-opencode

Config printers:

frontend-design-loop-setup --print-claude-config
frontend-design-loop-setup --print-codex-config
frontend-design-loop-setup --print-gemini-config
frontend-design-loop-setup --print-droid-config
frontend-design-loop-setup --print-opencode-config

Safety Defaults

  • custom commands are parsed as shell-free argv by default

  • shell syntax, substitutions, and inline interpreter execution like bash -c, python -c, and node -e require unsafe_shell_commands=true

  • preview_url must 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:

PYTHONPATH=src .venv/bin/python scripts/preflight_check.py

stdio smoke:

PYTHONPATH=src .venv/bin/python scripts/smoke_mcp_stdio.py

Built-in doctor:

frontend-design-loop-setup --doctor
frontend-design-loop-setup --doctor --smoke

Docs

Distribution State

Current public install path:

pipx install frontend-design-loop-mcp

Available Tools

1 tool
frontend_design_loop_evalD

MCP tool wrapper for _frontend_design_loop_eval_impl.

Returns:

  • JSON summary (TextContent)

  • Optional vision instructions (TextContent) when vision_provider=client

  • Optional screenshots as ImageContent (base64) so Claude can use built-in vision

ParametersJSON Schema
NameRequiredDescriptionDefault
goalNo
patchesYes
repo_pathYes
viewportsNo
preview_urlNo
vision_modeNoauto
lint_commandNo
test_commandNo
vision_modelNogemini-2.0-flash
keep_worktreeNo
include_imagesNo
gate_timeout_msNo
preview_commandNo
vision_providerNoclient
worktree_reuse_dirsNo
unsafe_shell_commandsNo
preview_wait_timeout_sNo
vision_score_thresholdNo
unsafe_external_previewNo
include_vision_instructionsNo
vision_broken_min_confidenceNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

D1.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. Only mentions return types; no disclosure of side effects, permissions, or limitations. Inadequate for safe invocation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is short but contains redundant info about return types (output schema exists). Lacks substance to earn its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 21 parameters, no annotations, and no schema descriptions, the description is critically incomplete. It doesn't explain tool behavior or parameter meanings.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 21 parameters with 0% description coverage in schema. Description adds zero parameter information, failing to compensate for the low coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description only states it's a wrapper for an implementation and lists return types, but does not specify what the tool does (e.g., evaluate frontend design). The purpose is vague and lacks a specific verb+resource.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool or alternatives. The description provides no context for usage decisions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev1.0.0
    • First observedfrontend_design_loop_eval

TDQS

C2.3/5.0

Scored across 1 tool

Disambiguation5/5

Only one tool exists, so there is no possibility of confusion with other tools. The tool's purpose is singular and unambiguous.

Naming Consistency5/5

With a single tool, naming consistency is trivially perfect. The name uses a descriptive underscore-separated pattern.

Tool Count2/5

A single tool for a 'frontend design loop' is far too few. The server promises a loop of design activities but only provides evaluation, missing creation, modification, or iteration tools.

Completeness1/5

The tool surface is severely incomplete. A design loop typically involves multiple steps (create, review, modify, test), but only an evaluation tool is provided, leaving critical gaps.

Maintenance

ActivityInactive
ResponsivenessNo issues

Related MCP Connectors

Related MCP Servers