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

Annotations declare readOnlyHint=true, and the description builds on this by revealing the methodology (90-day velocity vs 8 baseline windows), the exact fields returned (score, trend, z_score, recent_window_count, baseline_window_mean/std, window_counts), update frequency (daily), and historical depth (since 2004). This goes well beyond the annotation and gives the agent a thorough understanding of behavior.

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 two sentences with a logical flow: first the core function and output details, then explicit use-case triggers. Every phrase carries meaning—no filler. It's dense but not bloated, and front-loads the most important information.

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?

Even without an output schema, the description enumerates the return values and explains the data source, update cadence, and historical availability. Combined with the well-documented parameter schema and readOnly annotation, the agent gets everything needed to decide when and how to call it.

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?

The schema has 100% description coverage for the only parameter 'days', clearly explaining its optional nature and the history/Gold tier caveat. The main description adds no extra semantic detail about parameters, so it appropriately relies on the 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 ('Returns') and defines a concrete resource ('0-100 US food, dietary-supplement, and cosmetic adverse-event pulse') with data source and calculation (FDA CAERS 90-day report velocity vs 8 trailing baseline windows). It clearly distinguishes itself from generic data tools by naming the metric, domain, and timeframe.

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?

It explicitly states 'Call when the user asks about food or supplement safety surges, CAERS report spikes, or brand-safety risk, or when timing recall preparedness, ad pauses, quality investigations, or liability reserving.' This provides clear, specific invocation contexts, though it does not mention scenarios where the tool should NOT be used or point to alternatives.

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