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

Annotations already declare readOnly, idempotent, and non-destructive hints, and the description adds meaningful behavioral context beyond them: it does not consume quote allowance, omitting month returns the newest issue, and invalid or missing months produce specific error shapes (invalid_month vs issue_not_found with available_months). 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?

Every sentence earns its place: definition, return payload, use case, cost implication, sibling routing, parameter rules, error behavior, and comparability. The description is front-loaded with the core definition and no sentence is redundant with the schema or annotations.

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?

For a read-only lookup tool with an output schema, this description is complete. It covers purpose, usage context, alternatives, parameter format and defaults, error cases, cost side-effects, and issue comparability. There is no missing information an agent needs to correctly select and invoke the tool.

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%, but the description goes well beyond the schema's 'Issue as YYYY-MM. Omit for newest published issue.' It documents the valid date range from 2026-07, the one-issue-per-month cadence, omission behavior, and both error responses. The comparability guarantee across issues is also valuable semantic context.

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 verb ('Returns') and resource ('SMKlog parcel shipping price index'), then names exact outputs: cheapest purchasable service per box and lane, every rate behind it, and basket averages. It also distinguishes itself from sibling get_parcel_quote by clarifying it is reference data, not a specific shipment price.

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?

Provides explicit usage context with example questions ('what does shipping a shoebox cost in the US right now') and states that reading spends no quote allowance. It explicitly routes specific-shipment pricing to get_parcel_quote instead, making the when-to-use/when-not-to-use boundary unambiguous.

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