wage_benchmark

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

There are no annotations, so the description carries the burden. It discloses that this is a DEMO and should be verified against KNBS data, which is valuable. However, it does not explain behavior like data sources, return format, or edge cases, leaving many details undisclosed.

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 very concise, a single sentence plus a caveat, with no redundant information. It wastes no words and effectively front-loads the core purpose.

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?

Despite its brevity, the description covers the key purpose, target population, currency, and a reliability caveat. With a simple parameter set and an output schema present, this is sufficient for an agent to understand the tool's function and limitations.

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 0%, but the description does add meaning by noting experience levels (entry/mid/senior) and currency (KES). It does not explain the county parameter or the specific values for job_title, leaving some gaps but adding useful context beyond the schema.

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 the tool provides a monthly wage benchmark for Kenyan jobs and specifies currency (KES) and levels (entry/mid/senior). It distinguishes from siblings like job_match or skills_gap_analysis by focusing on wage data, though it lacks an explicit verb like 'returns' or 'gets'.

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 purpose implies when to use it (for wage benchmarks in Kenya), and the DEMO warning gives context about reliability. However, there is no explicit guidance on when to use this tool versus alternatives, nor any exclusions.

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