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Glama
Manavarya09

DarkLens MCP Server

by Manavarya09

classify_pattern

Classify a detected dark pattern by its ID to reveal its category, cognitive bias, and severity.

Instructions

Classify a detected dark pattern.

Args: pattern_id: The ID of the pattern to classify

Returns: Dictionary with category, cognitive_bias, severity

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries the behavioral burden. It states the return dictionary and implies a non-mutating analysis operation, but it does not disclose error behavior, whether the pattern_id must exist, or any permissions/side-effect information. This is acceptable but minimal.

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 a compact docstring with a one-sentence purpose, a single Args entry, and a Returns line. Every sentence contributes information 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.

Completeness3/5

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

For a single-parameter tool with an output schema, the description covers the basics and is enough to invoke if an agent already has a pattern_id. However, with no annotations and a sibling group containing similar dark-pattern tools, it would benefit from an explicit prerequisite or a pointer to detect_dark_patterns.

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 0%, so the Args line is the only explanation of pattern_id. It adds that the ID belongs to the pattern being classified, which is slightly more than the schema title 'Pattern Id', but it does not explain where the ID comes from or any format expectations.

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 opening line 'Classify a detected dark pattern' names a specific verb and resource, and the Returns line ('category, cognitive_bias, severity') clarifies exactly what classification produces. This distinguishes it from sibling tools like detect_dark_patterns, explain_manipulation, and risk_score.

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 word 'detected' implies the tool operates on an existing pattern, suggesting it should be used after detection, but the description never explicitly says when to prefer classify_pattern over siblings such as explain_manipulation or risk_score. No exclusions or alternative conditions are provided.

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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