Average German second-home tax (Zweitwohnungsteuer) rate in percent per Bundesland (federal state), aggregated over the levying cities with a computable rate — the data behind the Deutschlandkarte. Answers 'which German state has the highest/lowest second-home 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 carries full behavioral disclosure. It explains the aggregation methodology, the population of cities included ('levying cities with a computable rate'), and the important caveat that the result is an average over cities rather than an official state rate. This goes beyond a generic 'get data' description, though it does not cover data freshness or exact source details.
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 compact and information-dense, delivering purpose, scope, methodology, and a caveat in three sentences. It is front-loaded with the key value proposition and avoids 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 parameterless, read-only data lookup with no output schema, the description is sufficiently complete for an agent to select and invoke it correctly. It explains what the data is, how it was aggregated, what caveats apply, and what question it answers, making it self-contained for its simplicity.
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 and the schema has 100% coverage (empty properties object). With no parameters to document, the description cannot add parameter-level detail, but the context is clear that no input is required. Baseline of 4 is appropriate for a parameterless tool.
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 clearly identifies the tool as returning average second-home tax rates per German federal state, with a specific aggregation method (mean over levying cities) and the exact question it answers. It distinguishes itself from sibling tools like lookup (city-level) and ranking by focusing on state-level aggregation.
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 explicitly states the tool answers a state-level comparison question ('which German state has the highest/lowest second-home tax') and provides a critical caveat that this is not an official state rate because the tax is municipal. It implies when to use this tool over municipality-specific tools, though it does not name alternatives explicitly.
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.