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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so safety is covered. The description adds genuine value beyond that: the upstream source, the scope limit (US RxNorm vocabulary only, no strengths), the disclaimer that results say nothing about clinical appropriateness, and a cost of 1 credit. It does not describe the shape of the returned suggestions, which is the one remaining behavioural gap.

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?

Opens with the user-facing question, then delivers what it does, an example, the source, audience, scope limits and cost in tight succession. Every clause carries information — nothing is redundant with the annotations or schema beyond the necessary framing.

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 one-parameter, read-only suggestion tool with full annotation coverage and no output schema, the description covers purpose, source, scope limits and cost, which is enough to call it correctly. Only the expected form of the returned suggestions is unspecified.

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% and the single 'name' parameter is fully documented with the same 'as typed or heard' framing and the same 'ambienn' example used in the description. The description therefore adds essentially no parameter meaning beyond the schema, so the baseline 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?

States a specific verb and resource ('suggests correctly spelt drug names for a misspelt or misheard one') and anchors it with a concrete example ('ambienn' to 'ambien') plus the data source (NLM RxNorm spelling service). An agent can immediately tell this is spelling correction, not drug name lookup or interaction checking.

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?

Gives clear usage context — voice input, OCR, chat agents, typing from memory — which implies when to reach for it. It stops short of naming alternatives such as drug_name_match or drug_brand_names for cases where the spelling is already known, so routing among the many drug siblings is left to inference.

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