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

Annotations already declare a safe read-only, idempotent, non-destructive, closed-world profile, so the bar is lower. The description adds genuinely useful context beyond that: the data source (NebenkostenPro audits), continuous updating, and availability of methodology and citation notes.

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?

Front-loaded with the core purpose and the enumerated topics, then usage examples, then metadata notes. Slightly long because the English translation repeats the German content verbatim, which is duplication with little added value for an agent, but the structure itself is clean.

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?

With no output schema, the description does the work of indicating the return content (per-topic flag frequency, median flags per bill) and citing methodology/citation availability. For a zero-parameter statistics tool this is essentially complete; only the exact shape of the returned data remains unspecified.

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?

The tool takes no parameters, so the baseline is 4. There is nothing for the description to disambiguate, and it correctly implies the tool is parameter-free by framing itself as an always-available statistics lookup.

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?

States a specific resource (aggregate statistics from auditing real German utility bills) and enumerates the metrics returned: how often each topic is flagged and the median flags per bill. This clearly separates it from the sibling start_bill_check, which audits a specific bill rather than reporting aggregate findings.

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?

Gives concrete example questions it answers ('Wie oft sind Nebenkostenabrechnungen falsch?', 'Was ist häufig auffällig?'), which effectively communicates the aggregate/lookup use case. It does not, however, explicitly name the alternative (e.g. start_bill_check for one's own bill), so the routing is implied rather than stated.

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