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

The description goes far beyond the annotations (readOnlyHint, idempotentHint, destructiveHint) by explaining the logic behind sold estimate calculation (direct vs. calibrated with ratio disclosure), the composition of the asking band, and additional accompanying data like per-channel medians and MSRP retention. No contradictions 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?

The description is information-dense and front-loaded with the key purpose, then delves into details. While somewhat lengthy, every sentence contributes meaning. It is well-structured for an AI agent to parse, though slightly verbose.

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?

Without an output schema, the description fully covers what the tool returns: sold estimate with basis indicator, asking band with percentiles and sample size, per-channel medians, MSRP retention, and trade-in floor. This is comprehensive for a price statistics 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?

The input schema has 100% description coverage for the single config parameter. The description adds value by providing concrete examples of valid config keys (e.g., macbook-pro-14-m3pro-2023), which helps the agent understand the format beyond the schema text.

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 clearly states the tool provides two distinct slices: what a used Mac actually sells for (sold) and what sellers are asking (asking). It uses specific terminology like 'verified-sale percentiles' and 'calibrated ratio', and the purpose is well-differentiated from sibling tools like check_mac_listing or search_mac_listings.

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 advises to use the sold estimate as the price and asking to set a list price, and warns not to blend slices. However, it does not explicitly state when not to use this tool or mention alternative tools, but the context of price statistics is clear.

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