Swiss Monthly Gross Wages

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

Annotations already declare the operation read-only, non-destructive, and idempotent; the description adds genuine context beyond them: data source (FSO Lohnstrukturerhebung), no auth required, biennial availability 2012–2024, CHF/month units, and a concrete example value. This enriches the agent's mental model 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?

The description is compact and front-loaded: purpose, return value, available years, filters, and a clarifying example each appear in a short, purposeful sentence. No sentence is redundant or filler.

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?

For a read-only, well-schema'd statistics tool, the description covers source, auth, scope, available years, units, and filters, while the output schema supplies return structure. It would be slightly stronger with an explicit pointer to sibling FSO tools for detailed regional/industry tables, but nothing needed to invoke it correctly is missing.

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%, so the schema already documents all three parameters, their allowed values, and defaults. The description repeats the gender/percentile filter semantics and adds a helpful example value, but it does not materially improve parameter comprehension beyond the high-coverage 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 opens with a specific verb and resource: 'Get Swiss monthly gross wage statistics from the Federal Statistical Office...' and immediately defines the return value: nationwide median/percentile monthly wages in CHF. It also differentiates its aggregate scope by stating 'all industries and professional levels combined,' which separates it from generic FSO table tools.

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 domain is clearly stated, so an agent can infer this is appropriate for aggregate national wage statistics. However, it never names alternatives such as swissfso.table.query or swissfso.table.metadata, nor does it explicitly say when not to use this tool, leaving routing among FSO sibling tools implied rather than explicit.

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