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

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds useful behavioral context beyond annotations, notably that it uses the 'jüngsten lückenlos verifizierten Publikationstag' and aggregates results by type, which clarifies data recency and ordering expectations.

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?

Three sentences, each earning its place: what is returned, when to use it, and how to filter it. The most important information is front-loaded, and there is no filler or repetition.

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 read-only tool with all optional parameters and no output schema, the description is sufficiently complete: it explains the return subject, aggregation, use cases, and filtering options. It could have mentioned the anzahl maximum explicitly, but the schema already carries that detail.

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 80%, so the schema already documents most parameters well. The description adds value by naming the three filter dimensions—Kanton, Meldungsart, and Stichwort—but it does not provide parameter-level details beyond what the schema offers.

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 states a specific deliverable: the latest fully verified publication day's structured company changes, counted by type. It is clearly differentiated from sibling tools like firma_suchen and firma_details by its briefing/aggregation purpose, though it does not explicitly name them.

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 explicitly lists when to use the tool: for a daily AI run, morning briefing, or automated market observation. It does not state exclusions or compare with alternatives, but the use cases are concrete enough to guide selection.

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