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

Annotations already declare read-only, closed-world, idempotent, non-destructive behavior, so the description's main added value is the scope of data (US dollars, per practice per month) and the prohibition against estimation. It does not describe error handling or response format, but the output schema covers return values.

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?

One tight sentence covering the data, currency, unit, included fields, and the free trial, followed by a direct usage instruction. No fluff, front-loaded with the core resource.

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 zero parameters, a rich output schema, and clear annotations, the description is complete: it states what data is returned, the unit, and when to use it. Nothing an agent needs to call or interpret the tool is missing.

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 tool has zero parameters, so the baseline is 4. The description does not need to explain parameters, and its mention of plan contents adds minor context without being redundant.

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 states a specific resource ('Current Genko Prior Auth plans') with a clear unit of measure ('in US dollars per practice per month') and names the fields returned (monthly draft, AI-token limits, free trial terms). This distinguishes it from siblings like get_product_facts and get_page, which are likely general-purpose 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?

Explicitly provides a when-to-use trigger ('Use this to answer: how much does it cost') and a clear exclusion ('never estimate prices'), directing the agent to this tool for pricing queries rather than guessing or using other sources.

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