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

Annotations already declare this a safe read-only, closed-world tool, so the description's job is to add context beyond that. It does: it discloses the data provenance (own daily measurements), the aggregation method (medians rather than averages), the 30-day reset window, the covered retailers, and the default behavior when no type is given. It does not mention coverage limits or latency, but for a read-only lookup this is solid added context.

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 front-loaded with the central question and then packs scope, method, sources, and default behavior into compact clauses with no filler. It is dense but well-ordered, and the trailing fallback clause is placed where the agent will notice it. Slightly long as a single run-on construction, but every clause carries information.

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?

There is no output schema, so the description must convey what comes back, and it does: median price and median price change per product type, or biggest movers when no type is supplied. Combined with the disclosed time window and data sources, an agent has enough to decide whether to call it and how. Only the sibling differentiation and any coverage caveats are 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 coverage is 100% and the parameter already carries an example, so the baseline is 3. The description goes beyond it by explaining the semantic consequence of omitting soort (fallback to biggest risers/fallers), which the schema does not state. It also clarifies that filtering is by product type such as televisies or airfryers, reinforcing the expected value shape.

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 concrete question (which product types became cheaper or more expensive) and names the specifics: 30-day window, retailers Coolblue/bol/Teufel, median price and median price change per product, based on own NuActueel measurements. That is a clear verb+resource+scope. It stops short of naming which sibling to use instead (prijsdalers, product_prijs, toestelprijs overlap thematically), so an agent must infer the distinction.

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 final clause 'Zonder soort: de grootste dalers en stijgers' effectively tells the agent what happens when the filter is omitted, which is useful usage guidance. However, there is no explicit when-to-use versus siblings such as prijsdalers (which sounds very close to 'grootste dalers') or product_prijs, and no statement of exclusions. Usage is implied rather than directed.

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