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guard_add_consent_point

Read-onlyIdempotent

Scans local Next.js forms for collected personal data like email, phone, CPF or name, then generates a ready-to-place LGPD consent checkbox component with policy link and insertion guidance.

Instructions

Adiciona o ponto de coleta de consentimento (LGPD) aos formulários de um projeto Next.js que coletam dado pessoal (e-mail, telefone, CPF, nome). Lê o código localmente (nada sai da máquina), acha os formulários sem consentimento e devolve um componente ConsentField (checkbox com base legal e link de política) mais onde inseri-lo. Não altera arquivos: aplique as mudanças devolvidas.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filesNo
project_pathNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.9.0

TDQS

A3.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, but the description goes well beyond them: it discloses that code is read locally and nothing leaves the machine, that no files are altered, and that the output is a ConsentField component plus the insertion location. This is exactly the behavioral context the annotations cannot convey.

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

Conciseness4/5

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

Three sentences, front-loaded with the purpose, then method, then output and the no-mutation caveat. Every sentence carries weight, though the parenthetical field list is slightly dense.

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

Completeness3/5

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

The description communicates the input method (local read), the returned artifact (ConsentField with legal basis and policy link plus insertion point), and non-mutation, which compensates for the absent output schema. However, the two undocumented parameters remain a real gap for a tool an agent must invoke correctly.

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

Parameters2/5

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

Schema description coverage is 0% and the description never mentions the two parameters (files with path/content, and project_path). It only alludes to reading local code, leaving the agent to infer the meaning and format of both inputs from the schema alone.

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?

States a specific verb+resource: adding an LGPD consent collection point to forms in a Next.js project that collect personal data (e-mail, telefone, CPF, nome). This scoping to Next.js forms distinguishes it from siblings like guard_generate_cookie_banner and guard_generate_policy at a glance.

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

Usage Guidelines3/5

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

The description implies context for use (Next.js projects with forms collecting personal data) but never states when to choose this over sibling tools such as guard_make_compliant or guard_check_compliance. No explicit alternatives or exclusions are given, so routing relies on inference.

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