Skip to main content
Glama
alexalexalex222

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

frontend_design_loop_eval

Evaluates frontend design changes by applying patches, running tests/linting, capturing screenshots, and using vision AI to score visual quality and detect issues.

Instructions

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

Input Schema

TableJSON 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

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
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.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/alexalexalex222/frontend-design-loop-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server