get_drug_label

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

With no annotations, the description carries the full behavioral burden. It reveals key behaviors: labels are per product, matches may be combination products or a specific manufacturer's generic, long fields are truncated and flagged, and an empty result is returned with a note when there is no match. This is meaningful transparency beyond a simple read statement.

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?

Every sentence adds value: the purpose, the scoping use case, the per-product caveat, the truncation warning, and the Args/Returns sections. It is appropriately sized for a single-parameter tool with no annotations, and the main use is front-loaded.

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?

The description covers the purpose, parameter semantics, return contents, truncation behavior, and no-match handling. It does not discuss potential errors or authentication, but for a read-only label lookup with one parameter, the provided context is nearly complete and sufficient for an agent to call it correctly.

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%, but the description fully compensates by explaining the only parameter: 'drug_name: Generic or brand name, e.g. "metformin" or "Glucophage"'. It specifies accepted forms and provides concrete examples, so an agent knows exactly what to pass.

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 opens with a specific verb and resource: 'Read the FDA-approved product label for a drug.' It then clarifies the purpose by stating what to use it for (approved indications and labeled warnings), which distinguishes it from sibling tools like get_adverse_events and search_drug_recalls.

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 phrase 'Use this for what a drug is approved to treat and what its labeled warnings say' gives clear contextual guidance for selecting the tool. It does not explicitly name alternatives or state when not to use it, but the sibling tool names and the FDA-label framing make the distinction reasonably clear.

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