skill_demand

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

Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds valuable behavioral context: it explicitly notes the tool always covers the whole dataset (no filtering), and details the exact metrics returned (count, share, median salary with/without). This goes beyond the annotations and helps the agent understand the scope and output characteristics. No contradictions.

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 short paragraphs: the first immediately states the purpose, the second details the exact output fields, and the third clarifies the no-argument behavior. Every sentence contributes new information—there is no fluff, and the key message is front-loaded.

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 the tool has an output schema (the return values are structured) and no parameters, the description covers everything an agent needs to decide whether and how to call it: the exact skills, the metrics, and the fact that it operates on the whole dataset. There are no missing operational details (e.g., rate limits, authorization) and the scope is unambiguous.

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?

There are no parameters, and the schema confirms an empty properties object. The description repeats that it 'Takes no arguments' and 'always covers the whole dataset', which adds a small semantic meaning (the scope is fixed). Since there are zero parameters, the baseline of 4 is appropriate; the description does not need to explain parameter formatting.

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 verb ('Show') and resource ('how often each tracked skill appears in postings, and its salary effect'), and explicitly enumerates the skills covered (python, sql, excel, tableau, aws, spark). It is clearly distinct from siblings: search_jobs is about filtering postings, and salary_benchmark likely benchmarks salaries, while this aggregates skill demand across the whole dataset.

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 states that it takes no arguments and always covers the whole dataset, which implies it's for a global overview. However, it does not explicitly explain when to choose this over the sibling tools (search_jobs, salary_benchmark) or mention any exclusions. The usage context is implied but not directly contrasted with alternatives, leaving the agent to infer.

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