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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, not open-world), lowering the bar. The description goes beyond them by disclosing the dataset vintage (2025 published + 2026 COLA est) and the shape of the returned content (base amount, dependent modifiers, annual amount, IU/TDIU/SMC/P&T explanations), which matters since no output schema exists.

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?

Two sentences, front-loaded with what it is before when to use it. Every clause carries information: scope, vintages, return contents, and trigger queries, with no filler.

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 no output schema, the description carries the return-value burden and does so (amounts, modifiers, explanations). Parameters are fully schema-documented and annotations cover safety, so nothing an agent needs to call this correctly 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?

Schema description coverage is 100%, so the baseline is 3 and the schema already documents both parameters. The description adds mild context by expanding on dependent modifiers, but it doesn't explain parameter interactions and even references 'parents' modifiers that the dependents enum does not include as a selection.

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?

States a specific resource ('VA disability compensation monthly rate tables') with explicit scope (ratings 10-100% × dependent configurations) and two data vintages. An agent immediately knows this is a lookup-the-rate-table tool. It does not, however, explicitly distinguish itself from the sibling calculate_va_benefits, so it stops short of a 5.

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?

Gives concrete triggering queries ('how much is X% rated worth?', 'what does adding a dependent change?'), which is strong usage context. It stops short of naming an alternative or a when-not-to-use condition, so it is clear context without exclusions.

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