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

The annotations already establish read-only, idempotent, non-destructive, open-world behavior, so safety need not be restated. The description does add real behavioral value: results are sorted cheapest-per-litre, mixed purposes or strain proportions get flagged, and a caveat is surfaced in the result field 'zastrzezenie'. It stops short of explaining result size, ranking edge cases, or what happens when no match exists.

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 and ranking rule are front-loaded, and the caveat note earns its place by explaining a result field that has no output schema behind it. The trailing module/hub boilerplate is provenance rather than guidance, and the middle sentences could be tightened.

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 one-parameter, read-only lookup with no output schema, the description covers what is returned (composition-matched alternatives, price ranking, flags, caveat field) well enough to call and interpret it. Only the no-result / empty-result behavior is left unstated.

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 single parameter 'nazwa' is already documented as the commercial culture name, which the description mirrors without adding format or matching rules (e.g. exact vs fuzzy name matching). With one fully documented parameter, 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 first sentence names a specific verb-like task (finding a replacement culture) plus a value proposition (cheaper), and the second pins down the selection rule: identical species composition, ordered by cost per litre of milk. That is far more specific than the name alone, though it never explicitly states how it differs from sibling lookups such as serowarnia_kultura or serowarnia_szukaj_kultur.

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 framing 'Czym zastąpić daną kulturę i co wyjdzie taniej' tells an agent this is the tool for substitution/price-comparison questions about a single named culture. There are no explicit exclusions or named alternatives, so the agent must still infer that plain culture lookups belong elsewhere.

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