Average German dog tax (Hundesteuer, first dog per year) per Bundesland (federal state), aggregated over the covered largest cities — the data behind the Deutschlandkarte. Answers 'which German state has the highest/lowest dog tax'. Note: a mean over cities, not an official state rate (the tax is municipal).
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description takes on full disclosure duty. It transparently reveals that the value is a mean over cities, not an official state rate, and clarifies the municipal nature of the tax. This goes beyond the basic name and adds critical interpretive context, though it doesn't mention data source or update cadence.
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?
Two sentences, front-loaded with the core purpose and a concise caveat at the end. Every sentence adds value, no redundancy or fluff. The structure is ideal for a simple zero-parameter aggregation tool.
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 has no parameters, no output schema, and no annotations, the description provides sufficient context: what it returns, how it's aggregated, and a key caveat. It is complete for the tool's simplicity and likely usage.
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 has zero parameters, so per the rubric baseline is 4. The description provides context about what the returned data represents (average per state over covered cities), enhancing understanding of the output despite having no parameter semantics to clarify.
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 uses a specific verb+resource: 'Average German dog tax per Bundesland', which clearly states the tool's function. It distinguishes itself from siblings by focusing on state-level aggregations ('by_state') and explicitly mentions answering which state has the highest/lowest tax, differentiating from ranking or lookup tools.
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 clearly implies when to use the tool: for comparing dog tax across German states, and notes it aggregates over large cities. It does not explicitly name alternatives or exclusions, but the context of state-level aggregation versus the sibling tools' city-level lookup/ranking is evident from the wording.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct tax/domain and operation: state-level aggregation, per-city lookup, ranking, and yearly change tracking are clearly separated per tax. The only potential overlap between state averages and city rankings is resolved by explicit descriptions of the state vs. city scope.
The two tax areas follow a perfectly parallel pattern: <tax>_by_state, <tax>_changes, <tax>_lookup, and <tax>_ranking. The utility tools list_datasets and pfaendung_calc also use clear snake_case names and do not disrupt the overall convention.
Ten tools is well-scoped for a server covering two municipal taxes and a garnishment calculator. Each tax has four natural query operations, plus dataset discovery and a separate calculation tool, with no redundant or missing categories.
For a read-only data server, the surface is complete: per-city lookup, ranking, state aggregation, and change tracking exist for both taxes, list_datasets exposes dataset metadata, and pfaendung_calc covers the garnishment computation. There are no obvious dead-end workflows within the stated domain.