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

With no annotations, the description carries the behavioral disclosure burden. It reveals that the output is a historical series, that only certain metrics have history, and that SECO unemployment averages have a start boundary at 1993. It does not describe what happens when from_year is omitted, the response shape, or error/empty-data 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 purpose is front-loaded in the first clause, followed by a compact, high-value list of valid metrics and the SECO caveat. Every clause earns its place and there is no redundant restatement of the schema.

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 simple read-only trend tool with three parameters and no output schema, the description covers the main invocation points: single canton, valid metrics, and optional from_year. The main missing context is the output structure and default start year, which is a modest gap given no output schema or annotations.

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 0%, so the description must compensate. It enumerates each valid metric value and explains that metric keys come from list_metrics, and it notes optional from_year. It still does not define the canton format or constraints on from_year, but it materially improves parameter understanding beyond the bare 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?

Description opens with a specific verb and resource: 'Historical year-series for a metric in one canton,' immediately distinguishing get_trend from sibling tools like compare_cantons or rank_by_metric. Examples of supported series (sunshine to 1884, migration to 1981) anchor the intended use.

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?

It tells the agent to use a metric key from list_metrics, restricts to metrics that have history, and lists the accepted metric names, plus optional from_year. It does not explicitly state when not to use it or name alternative tools, but the 'one canton' scope and metric filtering give clear context.

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