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

Annotations already cover safety (readOnly, idempotent, non-destructive), and the description adds useful behavioral details like returning self-text instead of URL for Ask/Show and listing the exact fields returned. No contradictions found.

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?

Two sentences, front-loaded with the main action, and every clause adds value (types, usage guidance, return fields). No fluff or repetition of schema content.

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?

The tool is simple with 0 required parameters, and the description covers return values explicitly, which compensates for the lack of an output schema. Combined with strong annotations, this is nearly complete for an agent to invoke successfully.

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% for both parameters, so the baseline is 3. The description adds semantic clarity beyond the schema by mapping enum values to human-readable concepts ('new (newest)', 'ask (Ask HN)', 'job (jobs/hiring)'), exceeding the baseline.

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 opens with a specific verb and resource ('Fetch Hacker News stories') and enumerates the supported types. It clearly distinguishes from siblings like get_top_stories (which is likely a single fixed type) and get_hn_comments.

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 'PREFER for' phrase gives explicit user-intent examples ('newest HN stories', 'Show HN', etc.), which is strong when-to-use guidance. It does not explicitly mention when-not to use or name alternative tools, so it falls short of the top score.

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