skill_demand

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

Annotations already declare readOnly, idempotent, open-world and non-destructive behavior, so the safety profile is covered. The description adds genuine behavioral value by stating the output includes a delta between the oldest and newest month, which is not derivable from the schema. It stops short of noting aggregation or sampling caveats.

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?

A single tight sentence with no filler, and the core measure is front-loaded. The trailing clause about the oldest-to-newest change is slightly awkwardly worded but still earns its place as behavioral content.

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?

An output schema exists, so return shape need not be explained, and all three parameters are documented in the schema. What the tool computes and that it includes a trend delta is clear; only sibling routing guidance is absent.

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 100%, with months, skills and response_format all documented in the schema itself (including the tuned-matching note for known skills). The description adds no parameter-level syntax or constraints, so the baseline 3 applies.

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 names a specific computation — monthly share of job posts mentioning a skill, plus the change from oldest to newest month — so an agent knows exactly what is produced. It does not, however, differentiate itself from the sibling 'rising_skills', which sounds like an adjacent trend-over-time tool.

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 measurement it performs implies a use case (tracking demand for named skills over time), but there is no explicit statement of when to pick this over 'rising_skills' or 'hiring_threads', nor any exclusion. Usage is left to inference.

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