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

Annotations already mark it as a safe read operation, and the description adds substantial context: paid cost model (x402 USDC on Base, $0.002 per successful call, PaymentRequired object on unpaid calls), omitted usernames for privacy, and the exact response fields. This goes well beyond 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.

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

The description is front-loaded with purpose and usage, then details, alternatives, and payment information. It is dense but every clause is informative for correct invocation; the length is justified by the payment and privacy disclosures.

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?

Though there is no output schema, the description enumerates the returned fields, scope (up to 30 stories, one list), payment behavior, and alternative endpoints for related tasks. An agent has enough to call it correctly and understand the response shape.

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 coverage is 100%, so the baseline is 3. The description adds meaning beyond the schema by clarifying that 'top' means 'front page' and reinforcing the 1-30 limit, though the list values themselves are largely repeated from the schema.

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 and resource: 'returns up to 30 stories from one Hacker News list' via the official HN API. It distinguishes the tool from search/thread alternatives and clarifies the exact list types and output fields. The vague tool name 'hn' is rescued by the detailed description.

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?

It explicitly says when to use it ('to see what the tech/startup community is reading right now or to monitor launches') and when not to use it ('For full-text HN search and complete comment threads use ScrapeMole Social' with specific endpoints). The alternatives and conditions are named directly.

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