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

Annotations already cover read-only, open-world, idempotent, and non-destructive traits. The description adds useful behavioral context: it returns daily observations, requires no auth, uses an open license, and names the source dataset. This goes beyond the annotations without contradicting them.

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: the first sentence states exactly what data is retrieved, followed by return frequency, use cases, source, and licensing. Every sentence contributes selection-relevant information, with no filler or repetition of schema fields.

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 the tool's low complexity (one optional parameter), rich annotations, and existing output schema, the description is complete. It covers data content, source, license, authentication requirements, current rate level, and intended use cases, so an agent has everything needed to invoke it correctly.

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 100%: the only parameter, 'limit', is fully documented in the schema with bounds and default. The description does not add parameter-specific semantics beyond implying a 'requested period,' but with full schema coverage, the baseline of 3 is appropriate.

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 names a specific verb ('Retrieve') and a precise resource: SNB policy rate, SARON, special rate, and sight deposit rates. It clearly distinguishes this tool from finance/data siblings by tying it to Swiss National Bank monetary policy and its exact data cube. The title reinforces the scope, and the description gives enough specificity that an agent can select it without opening the schema.

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 description gives explicit context for when the tool is relevant: Swiss monetary policy analysis, CHF rate expectations, and fixed-income research. It does not explicitly list alternatives or when-not-to-use cases, so it stops short of a 5, but the use-case framing provides clear guidance for agent selection.

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