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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context beyond that by revealing the 25M+ record dataset, listing the exact returned fields, and noting the data is US public domain. No contradiction with annotations.

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?

Four substantive sentences: the search capability, a key-field summary, query-syntax examples, and provenance. Information is front-loaded and no sentence is wasted; the acronym expansion and source note add real context without bloating the description.

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 tool has an output schema, full parameter descriptions, and safety annotations. The description fills the remaining gaps by naming the database, its size, field vocabulary, query syntax, and public-domain status. An agent has everything it needs to decide when to call this tool and how to construct a valid query.

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 100%, so the baseline is 3. The description adds value by teaching Lucene syntax with concrete field examples (device.brand_name:'pacemaker', event_type:'Death') and clarifying that search supports event-type, brand, and other field filters. There is a minor inconsistency between the schema example ('device_name') and the description ('device.brand_name'), but overall the description strengthens parameter understanding.

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 ('Search') and a named resource ('FDA MAUDE database') and immediately defines the subject matter as medical device adverse event reports. It differentiates itself from sibling tools by emphasizing MAUDE, malfunctions/injuries/deaths, and a distinct return-field set, so an agent can distinguish it from openfda_devices.safety.recalls or clearance 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 Lucene examples and field listing make it clear the tool is for querying adverse event reports, but the description never explicitly states when to use this over sibling alternatives such as openfda_devices.safety.recalls or openfda_devices.reference.classification. Usage context is implied, not explicit.

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