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

Annotations already mark the tool as read-only and idempotent. The description adds behavioral context: the score is derived from frequency × average move size, and it never invents a score from a single point. This is valuable but could mention data recency or source.

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 two sentences, no filler, front-loaded with the core purpose. Every word adds value.

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 no output schema, the description adequately explains the return: volatility score, change count, net %, last change, and a sufficient flag. It is complete for a straightforward data-retrieval 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% with descriptions for both parameters. The description mentions 'kind' and 'slug' but adds no new semantic information beyond what the schema provides. Baseline of 3 is appropriate.

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 it computes a 0-100 volatility score for one product or vendor, derived from recorded moves. It also lists specific outputs (change count, net %, last change) and distinguishes from sibling tools like 'most_volatile_in_category' by focusing on a single entity.

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 includes a 'honest gate' behavior that explains when the tool returns a non-score result (sufficient:false) due to insufficient data, guiding proper use. However, it does not explicitly compare to all sibling tools or state when to use alternatives.

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