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

Annotations already declare readOnlyHint and idempotentHint, so the safety profile is known. The description adds valuable behavioral context beyond this: it clarifies the semantics of commercial='none' (not unpublished, but no accepted mapping) and hints that readers data is 'sampled observed', which is not in the schema. No contradiction with 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 a single, information-dense sentence that front-loads the core purpose and tacks on a precise clarification. Every word earns its place; no redundancy or fluff.

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?

With 12 parameters and no schema descriptions, the description is only partially complete. It omits guidance on the search query (q), sorting (sort), pagination (limit/cursor), and the meaning of 'sampled observed readers'. The output schema exists, so return-value documentation is not needed, but the missing usage details leave gaps 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?

Schema description coverage is 0%, so the description must compensate. It names most filter dimensions (genre, lifecycle, followers, readers, updated_days, commercial) but leaves q, sort, limit, cursor undocumented and does not explain min/max range semantics. The commercial clarification adds meaning, but coverage is incomplete.

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 verb ('Search') and resource ('Scrykeeper's Royal Road market dataset'), and enumerates the filter dimensions (genre, lifecycle, followers, readers, update recency, commercial mapping). It distinguishes itself from siblings (get_author, get_data_context, get_story) by being a dataset-wide search rather than a single-entity fetch.

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 implies usage for searching market data but provides no explicit when-to-use vs. alternatives guidance. It does not mention exclusions or scenarios where a sibling tool would be preferred, leaving the agent to infer from the sibling names.

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