ai-disclosure-kit

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

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=false. The description adds that the service is free, requires no API key, and returns the same disclosure preview shown before checkout – contextual details beyond the annotations that help an agent call it correctly.

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 front-loaded with the core purpose, then succinctly lists coverage and returns. The added notes about free preview and personalization are concise and useful; no wasted words.

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 simple read-only tool with only 2 parameters and no output schema, the description fully covers what it does, which touchpoints it handles, what it returns, and how to personalize. No critical gaps remain.

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 100%: both `touchpoint` and `businessName` have detailed descriptions in the schema. The description reiterates the personalization feature but adds no extra semantics beyond the schema. Baseline of 3 applies.

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 states a specific verb ('Check') and a precise resource ('whether the EU AI Act's Article 50 transparency rule applies') and the outcome ('get a free sample disclosure notice'). It enumerates the covered touchpoints (chatbot, synthetic media, public text, biometric), making its domain clear and distinct from the sibling get_ai_disclosure_kit_info.

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 clearly implies when to use the tool – when you have an AI touchpoint and want to determine Article 50 applicability and obtain a sample notice. It does not explicitly name alternatives or exclusions, but the context is unambiguous and the sibling purpose is sufficiently different. No misleading guidance.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, establishing this as a safe read operation. The description adds context about the kit's contents and the one-time $59 price, which helps the agent set expectations, but it does not describe the response format or whether any network access or authentication is involved. Still, with annotations covering the safety profile, this is adequate.

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 two tightly packed sentences. The first states the core function and lists deliverables; the second adds pricing and a concrete use case. Every clause earns its place, and the key information is front-loaded.

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 parameterless, read-only info tool with no output schema and no siblings, this description is fully sufficient. It tells the agent what the tool does, what content is covered, what it costs, and when it is useful. Nothing essential is missing for correct invocation.

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 tool has zero parameters and 100% schema description coverage (an empty schema), so there is nothing for the description to add about parameter meaning. The baseline of 4 for parameterless tools applies here; the description correctly focuses on the tool's purpose rather than inventing parameter details.

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 begins with a specific verb and resource: 'Get pricing and details for AI Disclosure Kit.' It then enumerates exactly what the kit includes (Article 50 transparency text, disclosure banner, compliance record template) and its scope (chatbots, AI-generated media, biometric-categorization/emotion-recognition), leaving no ambiguity about what this tool returns.

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 states a clear use case: 'Useful for an agent implementing EU AI Act transparency disclosures for its user's product.' While there are no sibling tools to contrast with, this sentence gives the agent context for when to invoke the tool. It lacks explicit when-not-to-use guidance, but that is less critical with no siblings.

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