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Zone tendue status of a French commune

check_zone_tendue
Read-only

Is a commune in zone tendue, and which list (reduced tenant notice, or tax only)? · Une commune est-elle en zone tendue, et sur quelle liste (préavis réduit, ou fiscal seulement) ? — Checks whether a French commune is in the 'zone tendue' perimeter and — crucially — WHICH one: the first list (décret n° 2013-392 annex, 1,434 communes, agglomérations >50k) where the tenant's reduced 1-month notice applies, or the wider TLV tax perimeter added in 2023 where ONLY fiscal measures apply (widely mislabelled 'zone tendue' — the reduced notice does NOT apply there). Deterministic dataset lookup on DILA reference data (service-public.fr simulator dataset) — no AI involved. Provide insee_code, or commune (+ code_postal to disambiguate).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
communeNoCommune name, e.g. 'Lyon'. Used when insee_code is not provided.
insee_codeNoINSEE commune code (5 chars, e.g. 75056 for Paris, 2A004 for Ajaccio). Preferred when known — skips name resolution.
code_postalNo5-digit postal code — disambiguates homonym communes.

TDQS

A4.1/5.0
Behavior4/5

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

Beyond the readOnlyHint=true and openWorldHint=false annotations, the description adds valuable behavioral context: it is a 'deterministic dataset lookup on DILA reference data (service-public.fr simulator dataset) — no AI involved.' It also discloses the real-world consequences of each list, making the tool's behavior and semantic boundary much clearer.

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

Conciseness4/5

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

The description is information-dense and front-loaded with the core question, then the critical caveat about the two lists. There is minor redundancy from the bilingual repetition of the opening question, but overall every substantive piece of the explanation earns its place given the tool's complexity.

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?

Given the absence of an output schema, the description adequately conveys what the caller should expect conceptually: membership in the zone tendue perimeter and which list applies. It also covers the data source, determinism, and input disambiguation needs. It does not specify the exact output shape, but for a boolean/list lookup the semantic outcome is clear enough.

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?

Schema description coverage is 100% and the schema already explains the commune name, INSEE code preference, and postal-code disambiguation. The description essentially restates this guidance ('Provide insee_code, or commune (+ code_postal to disambiguate)') without adding new parameter-level meaning beyond the schema.

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?

The description uses a specific verb and resource: 'Checks whether a French commune is in the zone tendue perimeter' and immediately distinguishes the two lists (reduced notice vs. tax-only). This clearly separates the tool from sibling tools and makes its exact scope unambiguous.

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

Usage Guidelines4/5

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

It gives clear usage context by explaining the critical distinction between the two 'zone tendue' lists and warns that the wider TLV perimeter is often mislabelled. It also tells the caller how to provide input ('Provide insee_code, or commune (+ code_postal to disambiguate)'), though it does not explicitly compare against sibling tools or state when not to use it.

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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TDQS

A4.4/5.0
Disambiguation4/5

Each tool is tied to a specific legal or payroll calculation, and the names clearly indicate their target: salary, rent revision, tenant notice, DPE restrictions, quasi-resident status, zone-tendue lookup, family allowances, and health insurance. Some overlap exists because rent revision and DPE checks both involve F/G rent freezes, and tenant notice relies on zone-tendue data, but descriptions keep boundaries clear.

Naming Consistency5/5

All tool names follow the same snake_case verb_noun pattern with a concise legal/domain object. The verbs vary by nature of action—calculate_, check_, compare_—but each is consistent and semantically meaningful.

Tool Count5/5

Eight tools is a well-scoped number for a specialist compliance server. Each tool handles a distinct calculation or legal lookup, and none feels redundant or purely decorative.

Completeness4/5

The set covers the main rental-revision, tenant-notice, DPE-restriction, zone-tendue, and cross-border worker salary/benefit comparisons well. Minor gaps remain around related compliance details such as rental deposits, landlord-side termination, or deeper Swiss tax filing checks, but they are not fatal to the tool set’s stated purpose.

Resources