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

Annotations already mark the tool as read-only. The description adds useful behavioral context: data source, time range, weekly update cadence, return components, and the meaning of high/low scores. It does not mention pagination, rate limits, or history-tier requirements, but those are less critical for a read-only data query.

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 information-dense without fluff: each clause adds value—methodology, interpretation, return fields, use cases, update frequency. It is well-structured and easy to scan, with the most essential information 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?

Given the tool's low complexity (one optional parameter), the description is complete: it explains what is returned, what the index means, when to use it, and how often it updates. Since there is no output schema, the explicit listing of return components (score, trend, top_drivers, methodology_version) is sufficient.

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 covers the only parameter (days) fully, including its optional nature, purpose, range, and tier restriction. The description does not add parameter-level details, but because schema coverage is 100%, the 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 opens with a specific verb and resource ('Returns a 0-100 weekly Drug-Safety Index'), clearly identifies the data source (openFDA adverse-event reports), and explains the scoring direction. It also enumerates the return components, making the tool's purpose unmistakable and distinct from generic safety or adverse-event tools.

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 explicitly states when to call the tool: 'Call when the user asks about drug adverse events, FAERS surges, medication side effects, drug safety trends, or pharmacovigilance, or when timing safety escalations, recall monitoring, or pharma risk reviews.' It lacks explicit when-not-to-use or named alternatives, but provides strong contextual guidance.

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