get_adverse_events

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

With no annotations, the description carries the full burden and does so well: it discloses that results are aggregate counts, not individual records, that reports are voluntary and non-causal, that common drugs accumulate more reports, and that cross-drug comparisons are unreliable. It also mentions the zero-report fallback.

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?

The description is front-loaded with the core purpose, followed by return behavior, caveats, and parameters. Each sentence adds distinct value, and the structure is easy for an agent to scan.

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?

Given the tool's complexity, the description is complete: it explains the output format, parameter constraints, interpretation caveats, and edge-case behavior. The presence of an output schema reduces the need to describe return values in detail, and the description still covers them.

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%, so the description must explain the parameters, and it does: drug_name is defined as generic or brand name with an example, and top_n is given a numeric range, default, and semantic meaning. This fully compensates for the bare schema.

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 uses a specific verb ('Summarize') and a clear resource (FDA adverse event reports for a drug), and explicitly distinguishes itself from returning individual case records. This makes it easy to differentiate from siblings like get_trial, search_drug_recalls, and get_drug_label.

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 description clearly conveys when to use the tool: to get aggregate reported side-effect information for a drug. It also provides important interpretational guidance about not treating reports as causal, though it does not explicitly name sibling alternatives or state when not to use this tool.

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