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

With no meaningful annotations, the description carries the behavioral burden. It discloses that the saved CV is used, that inference results are returned for confirmation, and that apply=true saves the rules and re-matches the feed. This gives a clear picture of side effects without contradicting the 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?

Three sentences with no filler, front-loaded with the usage trigger and followed by behavior and preset options. Every sentence adds useful information.

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 description covers the main workflow, return value, and conditional persistence. It lacks detail on preset semantics and failure behavior when a sentence cannot be parsed, but for a 3-parameter tool with no output schema it is reasonably complete.

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 compensate. It explains sentence with concrete examples and apply with its side effect, but preset is only listed as 'balanced | broad | focused' without explaining what each preset means or how it changes inference. This leaves a meaningful parameter gap.

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 action: turning a plain-language sentence plus the saved CV into concrete targeting rules. It also clarifies the output (inferred rules and confirmation chips) and the optional apply behavior, making the tool's role clear even against siblings like update_targeting.

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 says 'Use when the person says what they are looking for in their own words,' which is a clear trigger condition. It does not name alternative tools or state when not to use it, but the context is strong enough for an agent to route to it appropriately.

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