salary_lv_wages

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does not state whether this is a read-only operation, what the response structure looks like, or any limitations (e.g., data availability, aggregation). It only lists data dimensions, leaving the agent to infer behavior.

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 a single terse phrase that is front-loaded with the topic and key breakdowns. It wastes no words, though it is more of a label than a full sentence.

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 and no annotations, the description should explain what data is returned (e.g., wage values, units, time span) and usage context. It lists breakdown dimensions but omits return format, filtering behavior, and any prerequisites, making it insufficient for reliable tool invocation.

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?

The input schema already describes both params (year with default, sector as NACE filter) with 100% coverage. The description's mention of 'sector' aligns with the schema but adds no new meaning; it does not explain how 'year' or 'sector' map to the output breakdowns.

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 this tool provides detailed wage data for Latvia, enumerating breakdowns (sector, occupation, gender gap, regional). However, it lacks an explicit verb and does not directly contrast with siblings like salary_overview, so it's clear but not fully differentiated.

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?

There is no guidance on when to use this tool versus salary_overview or salary_ai_risk. The description implies use for detailed Latvia-specific wage breakdowns, but no exclusions or alternatives are mentioned.

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