Zügle

favorable_municipalities

Ranks municipalities that are cheaper than, or within CHF 1000/yr of, a stated current home, by annual tax saving — municipalities near-identical in cost are included deliberately, not filtered out. Answered from cached figures only, so the ranking may be partial; the response states how many candidates were covered. Pass near (a town or station name) and radiusKm to keep only places around where the household lives or works; an unrecognised place is reported, not silently ignored.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`near`	No	Town or station name to search around, e.g. "Winterthur". Not a postcode.
`income`	Yes	Gross annual income in CHF.
`children`	No	Dependent children.
`radiusKm`	No	Distance from `near` in km (default 20). Ignored without `near`.
`confession`	No	Church-tax affiliation.	none
`currentBfsId`	Yes	BFS id of the current home.
`currentRooms`	No	Size of the current home in rooms, used for the rent comparison.
`relationship`	No	Marital status.	single

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedSep 10, 2026

addedInput schema / properties / near

Added value: +{
+  "description": "Town or station name to search around, e.g. \"Winterthur\". Not a postcode.",
+  "maxLength": 80,
+  "type": "string"
+}

addedInput schema / properties / radiusKm

Added value: +{
+  "description": "Distance from `near` in km (default 20). Ignored without `near`.",
+  "maximum": 100,
+  "minimum": 1,
+  "type": "integer"
+}

First observedSep 1, 2026

TDQS

A4.2/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must carry the behavioral burden, and it does: it discloses cached-only figures, potential partial rankings, the deliberate inclusion of near-identical municipalities, and error reporting for unrecognized places. This is substantial behavioral context beyond the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded: ranking criteria first, data caveat second, parameter guidance third. Every sentence contributes a distinct piece of information, with no repetition of schema fields.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an 8-parameter tool with no annotations and no output schema, the description covers the core decisions: what is ranked, on what basis, with what data limitations, and how errors surface. It does not describe the response shape beyond coverage count, but that is a minor gap given the strength of the rest.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already documents all 8 parameters, so adding param detail in the description is bonus. The description contributes a useful semantic link between `near` and `radiusKm` and the household context, but it does not materially expand parameter meaning beyond the schema's explanations.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States specifically what the tool does: ranks municipalities by annual tax saving against a current home, with a precise inclusion threshold (cheaper or within CHF 1000/yr). This clearly differentiates it from sibling tools like municipality_search or tax_lookup.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description tells the agent how to scope results via `near` and `radiusKm` and flags unrecognized places, but it never explicitly says when to choose this tool over siblings or what conditions favor alternatives. Usage context is implied by the ranking purpose rather than stated as a routing rule.

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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