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Manavarya09

DarkLens MCP Server

by Manavarya09

risk_score

Evaluate a UI pattern's legal compliance risk for a chosen region, returning a risk score, violated regulations, and enforcement likelihood to guide ethical redesign.

Instructions

Assess legal/compliance risk of a pattern.

Args: pattern_id: The ID of the pattern region: Geographic region (EU, US, India)

Returns: Dictionary with risk score, violated regulations, enforcement likelihood

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
regionYes
pattern_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations present, the description carries the full burden of behavioral disclosure. It conveys a read-only assessment operation and lists the return content, but it does not explicitly state side effects, data-source assumptions, or behavior for invalid regions or pattern IDs. That leaves some ambiguity, though nothing contradicts the annotations.

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?

The description is compact, front-loaded with the core purpose, and organized into Args and Returns sections. Every sentence adds information; there is no filler or repetition of the tool name.

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 two-parameter tool with an output schema, the description is nearly complete: it defines the operation, both parameters, and the return shape. The main gap is the lack of usage guidance relative to sibling tools, but that is already penalized in its own dimension. Nothing essential for calling the tool is missing.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must compensate. It adds meaning by explaining pattern_id as 'The ID of the pattern' and by listing the expected region values (EU, US, India), which the schema itself does not provide. The pattern_id explanation is thin, but the region guidance is genuinely useful.

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?

The description opens with a specific verb and object — 'Assess legal/compliance risk of a pattern' — which states exactly what the tool computes. The legal/compliance angle separates it from sibling tools like classify_pattern or detect_dark_patterns, so an agent can distinguish it without inspecting the schema.

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 is given about when to choose risk_score over siblings such as classify_pattern or explain_manipulation, and no exclusions or prerequisites are mentioned. The purpose statement implies a use case, but the description never tells the agent how to route to this tool versus alternatives.

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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