Ranking of German cities by Zweitwohnungsteuer rate: highest or lowest, plus the average, count with a computable rate, and count of cities with no second-home tax.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many cities to return | |
| order | No | expensive |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the output categories (ranking, average, count with computable rate, count with no tax) and the two ordering directions. It does not mention data source, update cadence, or edge cases, but the tool appears read-only and low-risk.
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 a single compact sentence that packs the core purpose, ranking directions, and all return metrics with no filler or redundancy. Every phrase contributes meaningful information.
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?
Despite lacking an output schema or annotations, the description explains what the tool produces well enough for selection and basic invocation. It doesn't specify the exact response format, but that is not critical for understanding whether to use the tool.
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 50%: 'limit' is described in the schema, while the description adds context to 'order' by mentioning 'highest or lowest', which maps to 'expensive' and 'cheapest'. This partially compensates for the missing enum descriptions, but no additional parameter-specific guidance is given.
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 opens with 'Ranking of German cities by Zweitwohnungsteuer rate', giving a clear verb, resource, and scope. It also distinguishes itself from sibling tools by explicitly covering highest/lowest rankings plus aggregate counts, unlike lookup or by_state 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?
Usage is implied through the word 'Ranking' and the mention of 'highest or lowest', suggesting comparison across cities. However, there is no explicit guidance on when to choose this over lookup or by_state variants, leaving the agent to infer from sibling names.
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.