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

Annotations already declare readOnlyHint=true, so the safe-read nature is covered by structured data. The description adds only the endpoint mapping ('openFDA drug/drugsfda endpoint') and dataset identity; it says nothing about pagination limits, default result count, or rate limiting beyond what the schema already states.

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 compact sentences with the dataset scope front-loaded and no wasted clauses. The trailing endpoint reference is mildly redundant but still informative for an agent mapping to the openFDA API.

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 no output schema, the description should ideally hint at the return shape (nested applications/products/submissions arrays), which it only lists obliquely. It covers dataset scope adequately but leaves result-structure and pagination semantics to the schema, which is workable but incomplete.

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%, so the schema fully documents skip, sort, limit, and the Lucene search syntax with an example. The description contributes no additional parameter meaning, making the baseline 3 appropriate.

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 names a specific verb (Search) and resource (Drugs@FDA: FDA-approved drug products and their applications/NDA/ANDA/BLA, sponsors, approval dates, products, submissions). This clearly distinguishes it from sibling datasets like search_drug_events, search_drug_labels, and search_drug_ndc, though it never names those siblings explicitly.

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?

Usage is only implied by the dataset scope: an agent can infer this is for drug approval/application data, but the description gives no explicit when-to-use guidance, no when-not-to-use, and no pointer to alternatives such as search_drug_events or search_drug_labels. Adequate but with a clear gap.

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