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

Beyond the annotations (readOnlyHint=true), the description adds valuable behavioral context: the index is computed as a 90-day volume weighted by FDA class, trended against the prior 90 days, and updates daily. It also discloses the exact output fields (score, trend, top_drivers, confidence, methodology_version), giving agents a clear picture of what to expect without an output schema.

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 dense but efficient, front-loading the primary purpose in the first clause, then adding output details, use cases, and update frequency. Every sentence earns its place, and there is no redundant wording or repetition of schema 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?

With no output schema, the description carries the full burden of explaining return values, and it does so thoroughly by listing all output fields. It also explains the time window, trend calculation, examples of top drivers, and the daily refresh. For a single-parameter tool, this is a complete and self-sufficient description.

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 input schema has 100% description coverage for the only parameter 'days', explaining its optional nature, range, and tier requirement. The tool description itself does not mention any parameters, but the schema fully compensates, so a baseline score of 3 is 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 begins with a specific verb ('Returns') and a clearly defined resource ('0-100 US food-recall activity index'), then enumerates the output components. It uniquely identifies this tool among a large sibling list by focusing on FDA food-recall enforcement activity, making it unmistakable.

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 provides explicit 'Call when' scenarios: food-safety recall pressure, contamination outbreaks, FDA enforcement activity, and specific decision contexts like supplier audits and QA sampling. It does not state when not to use it or mention alternatives, but the use cases are clear and practical.

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