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

The readOnlyHint annotation already establishes the safe-read profile, so the description's main added value is identifying the backing dataset/endpoint. It does not disclose pagination ceilings, result caps, or rate-limit behavior beyond what the schema states, but it does not contradict anything.

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 zero filler, front-loaded with the dataset identity and followed by the endpoint reference. Nothing is redundant with the title or wasted on restating the name.

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 filter/search tool with no output schema, the description adequately conveys what corpus is searched and against which endpoint. It stops short of describing result shape or the practical impact of the limit/skip caps, but the 100%-covered schema carries that load.

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 all four parameters (skip, sort, limit, search) are already fully documented, including the Lucene syntax and the unusual default limit of 1. The description adds no query syntax or field-hint detail 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?

The description names a specific verb ('Search') and a precisely scoped resource ('Drugs@FDA: FDA-approved drug products and their applications (NDA/ANDA/BLA), sponsors, approval dates, products and submissions'), plus the exact underlying endpoint. It cleanly separates this dataset from siblings like search_drug_labels, search_drug_events, and search_drug_ndc so an agent can route correctly.

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; there is no explicit 'use this when' statement, no prerequisites, and no named alternative (e.g., versus search_drug_labels or search_drug_ndc). An agent can infer the domain but must decide on its own when this is the right sibling.

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