List the available hand-verified German municipal tax/fee datasets (Hundesteuer, Zweitwohnungsteuer, Pfändung), their coverage, source sites and refresh cadence. Call this first to see what you can query.
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It clearly indicates a read-only listing operation via the verb 'List', and adds valuable context about the data being 'hand-verified' and including 'refresh cadence', which informs the agent about data quality and update frequency. This goes beyond a simple restatement and is fully transparent for a non-destructive discovery tool.
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 concise, with two sentences. The first sentence states the purpose and content, and the second gives a clear usage directive. Every word earns its place, with no redundancy or irrelevant detail.
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 simple listing tool with no parameters and no output schema, the description is complete. It covers what is listed (datasets, coverage, source sites, refresh cadence) and provides context ('Call this first') to guide the agent. The sibling tool names further clarify the context. Nothing essential is missing.
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 the baseline is 4. The description doesn't need to explain parameters, and it appropriately focuses on what the tool returns rather than parameter details.
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 the specific verb 'List' with a clear resource: 'available hand-verified German municipal tax/fee datasets'. It also names the specific datasets (Hundesteuer, Zweitwohnungsteuer, Pfändung) and distinguishes itself from sibling tools by describing its scope as an overview of coverage, sources, and refresh cadence rather than querying individual datasets.
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 'Call this first to see what you can query', giving clear guidance on when to use it as an entry point. It does not explicitly mention when not to use it or name alternatives, but the sibling tool names imply that the other tools are for specific queries, making the usage context sufficiently clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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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.