Ranking of German cities by Hundesteuer (first dog, per year): most expensive or cheapest, plus the national average across the covered cities.
| 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?
With no annotations provided, the description must carry the burden of behavioral disclosure. It does disclose the inclusion of a national average and the two ordering modes, but it omits details such as default order/limit, data source, or whether the average changes with the limit parameter. This is a reasonable but incomplete transparency profile.
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?
A single sentence front-loads the core purpose and packs in key specifics (first dog, per year, expensive/cheapest, national average) with no wasted words. Every element earns its place.
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?
The tool is relatively simple, and the description covers the main behavior (ranking, ordering, average) despite lacking an output schema. It does not specify the exact return structure, but the description is sufficient for an agent to invoke the tool for ranking scenarios.
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 50%; the limit parameter is documented in the schema, while order is not. The description adds meaning by mapping 'expensive' and 'cheapest' to the order enum, but it does not discuss limit or how the two parameters interact with the average. This partially compensates for the schema gap.
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 states the tool produces a ranking of German cities by Hundesteuer, with specific dimensions (first dog, per year) and options (most expensive/cheapest, plus national average). This is a specific verb+resource and is readily distinguishable from sibling tools like hundesteuer_lookup or hundesteuer_by_state.
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 establishes clear context: use this when you need a ranked comparison of cities by dog tax, including ordering by cost. It does not explicitly name alternatives or exclusions, but the ranking focus and comparison to sibling tool names imply when it is appropriate.
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.