detect_nsfw

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

With no annotations, the description fully carries behavioral disclosure. It reveals the output format (classification label and is_nsfw boolean), cost (2 sats per image), and payment mechanism (Bitcoin Lightning, no signup). It does not cover error handling or rate limits, but the core behaviors are transparent.

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 four sentences, each adding distinct value: purpose, cost advantage, return format, and payment instructions. It is front-loaded with the core action and avoids redundancy.

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?

Given the simplicity of the tool (two parameters, no output schema), the description covers all critical aspects: purpose, usage context, behavioral traits, and parameter semantics. It could mention error cases, but it is largely complete for a moderation tool.

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?

The input schema has 100% coverage with descriptions for both parameters. The description adds context by explaining how to obtain a paymentId (via create_payment with this tool name), which enhances the schema. No further parameter detail is needed.

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's purpose: classifying image safety into three categories (normal, suggestive, explicit). It distinguishes itself from siblings by emphasizing cost and speed advantages over LLMs, which is a unique selling point.

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 provides clear guidance on when to use the tool, calling it 'essential for content moderation pipelines.' It also explains the prerequisite payment flow (create_payment with toolName='detect_nsfw'), but does not explicitly name alternative tools or state when not to use it.

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