detect_dark_patterns

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

Since no annotations are provided, the description carries the behavioral disclosure burden. It does reveal the return shape (list with pattern_id, pattern_type, confidence, evidence), which is useful, but it does not disclose error behavior, input constraints, or whether the operation is read-only in any explicit way.

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

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

The description is concise, front-loaded with the main purpose, and uses a compact Args/Returns structure. Every sentence earns its place with no redundancy or filler.

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

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

For a two-parameter detection tool, the description covers the purpose, both parameters, and the return format. An output schema exists, reducing the need to explain return values further. It could be more complete by addressing how invalid input_type values are handled or how to choose between this and sibling tools, but it is largely sufficient.

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

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

Schema description coverage is 0%, but the description fully compensates by explaining both parameters: input_type values ('html', 'text', or 'url') and what content contains. This adds meaning well beyond the bare schema, which only names the parameters.

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

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

The description clearly states the tool detects dark patterns in UI text, HTML, or URL, specifying a concrete verb and resource. It is understandable, though it does not explicitly differentiate itself from related siblings like classify_pattern or analyze_text beyond its dark-pattern focus.

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

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

The description gives a clear sense of what input types are accepted, but it provides no guidance on when to use this tool versus alternatives such as classify_pattern or analyze_text. There are no exclusions, prerequisites, or explicit usage conditions beyond the basic input_type/content split.

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