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ui-polish-mcp

ui-polish-mcp

by Pedro21062014
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

refine_copy

Refine generic UI text to sound human and contextual. Replace placeholder phrases in button labels, placeholders, error messages, and empty states.

Instructions

Melhora textos genéricos de UI para soarem humanos e contextuais. Remove padrões como "Submit", "Click here", "Error occurred", "Lorem ipsum". Use em labels de botões, placeholders, mensagens de erro e empty states.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "text": "<text>"
    },
    "name": "refine_copy"
  }
}

Input Schema

NameRequiredDescriptionDefault
langNoIdioma (padrão: pt)
nounNoSubstantivo do contexto: projeto | post | usuário | relatório | mensagem
textYesTexto original para refinar
toneNoTom da escrita
actionNoAção do contexto: save | publish | delete | send | create | update | confirm

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the transparency burden and does disclose core behavior: it removes generic patterns and makes text sound human/contextual. It gives concrete examples of patterns removed, which helps set expectations, although it doesn't state output shape or edge-case behavior.

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

Conciseness5/5

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

Three short sentences front-load the main action, then give concrete examples and target UI contexts. Every sentence adds value and the structure is easy to scan.

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

Completeness4/5

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

For a text-refinement tool with 5 params and no output schema, the description covers purpose, target contexts, and expected transformations. It could mention the returned value or limitations (e.g., not for long-form copy), but the description is sufficient for basic selection and invocation.

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

Parameters3/5

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

Schema description coverage is 100%, so the baseline is 3. The description adds broader context about intended inputs (generic UI text and examples), but does not add parameter-specific detail beyond what the schema already provides.

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

Purpose5/5

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

Description uses a specific verb ('Melhora'/improves) and identifiable resource ('textos genéricos de UI'), then explicitly names the transformation ('Remove padrões como...'). It clearly distinguishes refine_copy from sibling tools by focusing on improving existing UI text rather than suggesting or auditing.

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

Usage Guidelines4/5

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

The description provides concrete contexts: 'labels de botões, placeholders, mensagens de erro e empty states.' It does not mention exclusions or alternative sibling tools, but the stated use cases are clear enough for selection.

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

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