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 clear. The description adds value by detailing the output shape (breakdown by status and phase) and the effect of filters on the output, which goes beyond what the annotations provide.

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 three sentences, front-loaded with the core action ('Count clinical trials...'), followed by usage context and filter behavior. No word is wasted; every sentence adds useful 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 an output schema present, the description doesn't need to detail return values. It covers purpose, usage, and filter behavior. Minor gaps: it doesn't specify how simultaneous status and phase filters interact or what happens when no trials match, but these are edge cases likely covered by the output schema.

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 coverage is 100%, so the baseline is 3. The description adds extra semantics by explaining that status/phase are filters that change the response to a single total without breakdown, contextualizing the condition parameter as the primary grouping entity. This enhances the schema descriptions.

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 ('Count'), explicitly names the resource ('clinical trials'), and specifies the breakdown by recruitment status and trial phase. This clearly distinguishes it from sibling tools like ct_search or ct_get_study, which retrieve individual trials rather than aggregate counts.

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 gives a clear use case: 'Use for landscape questions about how much trial activity a disease has and where it sits in development.' It also explains the behavior of filtering ('Filtering by status or phase returns the filtered total without the breakdown'). While it doesn't explicitly name alternative tools, the context is sufficient to guide appropriate use.

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