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

The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, and the description does not contradict them. It adds substantial behavioral context: rating is rounded to nearest 10 percent, dependents affect rates only at 30% and above, children are limited to under 18, and the result is the published table rate rather than an individualized payment or retroactive amount. This goes well beyond the annotation metadata.

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 front-loaded with the primary use case and then provides dense, non-redundant details about return values, rounding behavior, dependency rules, and exclusions. Every sentence earns its place; there is no filler or repetition of the title or tool name.

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 moderate complexity, three parameters, and absence of an output schema, the description is remarkably complete. It explains what is returned (monthly and annual amounts, rate year, effective date), what the model includes and excludes, and how edge cases like dependents and rounding are handled. This is sufficient for an agent to select and invoke the tool correctly without additional documentation.

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 already provides full descriptions for all three parameters (100% coverage), and the description reinforces and extends their meaning: it clarifies that the rating is read to the nearest 10 percent, that hasSpouse and childrenCount only matter at 30% and above, and that childrenCount refers to children under 18. This adds semantic value beyond the schema field names and defaults.

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 use case: 'Use this when a veteran wants the monthly VA disability compensation amount for a given rating percentage and dependent situation.' It clearly identifies the resource (VA compensation rate table), the action (look up monthly and annual amounts), and the key inputs (rating and dependents). It also distinguishes itself from back-pay calculations, separating it from the sibling tool compute_retroactive_pay.

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 use the tool and what is excluded: it is not a back-pay calculation, not what a specific veteran is paid, and it does not model school-age children 18–23, dependent parents, or aid and attendance. However, it does not name alternative sibling tools that should be used for those excluded cases, so it stops short of full alternative guidance.

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