Live income+wealth tax for a specific commune & household (official ESTV, year 2026). commune = name or postcode; income in CHF; married bool; children count; wealth in CHF.
| Name | Required | Description | Default |
|---|---|---|---|
| income | Yes | ||
| wealth | No | ||
| commune | Yes | ||
| married | No | ||
| children | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description partially compensates by stating the tool is 'Live' (real-time) and uses official data (ESTV). However, it does not disclose potential error handling, response format, or whether the tool has any side-effects. More details on behavior would be beneficial.
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 two sentences. The first sentence states the purpose concisely, and the second enumerates parameters with their formats. There is no unnecessary text, making it efficient and front-loaded.
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 description covers the input parameters well but does not describe the output (e.g., returns a tax amount in CHF). Given the absence of an output schema, this omission reduces completeness. It also lacks info on error states or required data validation. Overall, it is adequate for simple use but could be more comprehensive.
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 0%, so the description must add meaning. It explains that 'commune' can be name or postcode, specifies units for income and wealth (CHF), and clarifies that 'married' is a boolean and 'children' a number. This adds significant value beyond the raw schema, though it could elaborate on optionality and expected ranges.
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 it calculates a live income and wealth tax for a specific commune and household, specifying the source (official ESTV) and year (2026). This distinguishes it from sibling tools like compare_cantons or get_canton, which focus on broader comparisons or canton-level data.
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 implies usage when needing a tax estimate for a specific commune and household, but it does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or prerequisites. The guidance is implied but not explicit.
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 has a clearly distinct purpose: consultation, tax calculation, canton comparison, relocation cost estimate, single-canton data, historical trends, metric listing, ranking, commune search, and upcoming votes. No overlap or ambiguity exists.
All tool names follow a consistent verb_noun snake_case pattern (e.g., list_metrics, search_commune, estimate_net_move). No mixing of conventions or erratic naming.
With 10 tools, the server is well-scoped for its domain (Swiss living index). Each tool provides necessary functionality without being overwhelming or too sparse.
The tool set covers core CRUD-like operations: search, retrieve, compare, rank, trend, and estimate. Minor gaps exist (e.g., no direct tool for detailed commune-level rent or job data), but the overall coverage is strong for the stated purpose.