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

With no annotations, the description carries the full behavioral burden. It helpfully discloses the returned metrics and trend window, but it does not state that the operation is read-only, how fresh the data is, whether results are cached, or what errors occur for unknown brand/model combinations.

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 a single front-loaded sentence with no filler. It efficiently states the scope and returned metrics.

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?

The return metrics are described, which is useful because there is no output schema. But the required brand and model parameters have no schema descriptions, no annotations exist, and no alternatives are named, leaving the agent to infer formats and routing.

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 0%, so the description must define brand and model. It only says the tool operates on 'one brand+model,' which restates the parameter names without explaining acceptable formats, casing, whether model must be exact, or how to handle missing models.

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 (market trend) and the exact metrics returned: current median/avg price, listing count, and 7d-vs-30d trend for one brand+model. It is clearly narrower than get_market_overview, but it does not explicitly differentiate itself from get_price_history or get_price_movers.

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?

Usage is implied: call this when you need current market stats for a specific brand and model. However, there is no explicit when-to-use/when-not-to-use guidance and no mention of alternatives such as get_market_overview for broader data or get_price_history for historical series.

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