Query City Employment Data

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

No annotations exist, so the description carries the full behavioral burden. It usefully discloses the underlying source per country (BLS, ONS/Nomis, StatCan LFS) and coverage breadth, but says nothing about rate limits, auth, caching, or freshness of the statistics.

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?

Front-loads the purpose, then organizes source coverage in compact per-country lines with no filler. The structure is scannable and appropriately sized, though the final sentence slightly duplicates the schema's country parameter text.

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 2-parameter read tool with no annotations and no output schema, the description supplies the source landscape and country scope an agent needs to invoke it confidently. Return-value shape is unspecified, but that is a minor gap given the no-output-schema note.

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 both parameters are already documented, including the ambiguous-city list. The description only repeats the auto-detect rule and country set, adding little beyond the schema; 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?

States a specific verb and resource ('Query employment statistics for a city') and scopes the geography to US, UK, or Canada. It does not explicitly differentiate itself from close siblings like query_economics or query_demographics, so it stops short of a 5.

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?

Gives implied usage through the country-by-country data source breakdown and the auto-detection rule, but never states when to pick this over query_economics or compare_demographics. Guidance is inferable rather than explicit.

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