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

Annotations already declare readOnly/idempotent/non-destructive/open-world, so safety is covered; the description adds genuinely new behavior: results newest-approval-first, real offset+limit pagination backed by a total match count, and that the data is live. It does not state update cadence or whether filters combine with AND, which would have pushed this to a 5.

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?

Front-loaded with the verb and scope, then question intents, then the row shape, then pagination/ordering — a sensible information hierarchy in a single dense paragraph. The long field enumeration is long but it substitutes for a missing output schema, so it earns its place; only slightly verbose.

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 13 parameters, no output schema, and an open-world dataset, the description covers everything an agent needs: what the corpus is, what filters mean in question terms, the exact row fields returned, and the pagination/ordering contract. Nothing material is left for the caller to guess.

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% with inline examples, so the baseline is 3. The description goes beyond that by mapping natural-language questions onto specific filters (ingredient → active_ingredient, company → holder, ATC class → atc_code) and by noting that pagination is offset+limit with a total count, which tells the agent how to walk results.

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?

States a specific verb and resource ('Search medicines registered in Singapore') and immediately bounds the corpus (~5,500 HSA-registered therapeutic products) and live source (data.gov.sg). Four sample question phrasings make the retrieval semantics unambiguous and separate it from the single-product and change-feed siblings.

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 'Answers ...' sentence gives strong positive usage context (approval lookups, ingredient lookups, holder lookups, ATC-class lookups), so an agent knows what questions route here. It never states when NOT to use it or names the alternative siblings (hsa_sg_product for a single detail record, hsa_sg_recent_changes for changes), so the routing is implied rather than explicit.

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