Aucadastre

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context beyond annotations: the address input format, the fallback behavior when no street number is provided, the breadth of data returned, and the note that the text mirrors the road-page .md documentation.

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 packs a lot of useful content into a single dense run-on sentence, which is informative but hard to scan. The list of fact categories earns its place, and the input caveat is valuable, but the structure could be improved with clearer separation between scope and usage notes.

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 broad facts endpoint with no output schema, the description enumerates the major content areas and an important edge-case behavior. It is comprehensive enough for an agent to select the tool and understand what kind of data to expect, though it does not describe the response format or field naming.

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 coverage is 100%, and the schema already gives a clear example for the address parameter. The description adds the important semantic nuance that the number is optional and that omitting it shifts the response to the road reference point, which is not fully captured in the schema.

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 identifies the tool as a broad facts-gathering endpoint for a French address, enumerating concrete data categories such as cadastral parcel, urban zoning, risks, terrain, permits, sales, diagnostics, schools, and companies. This distinguishes it from the sibling tools, which each focus on a narrower slice (permits, commune, parcel).

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 gives useful input guidance ('address in full words; without a number, the response applies to the road reference point'), but it does not explicitly state when to use this tool versus siblings such as get_french_parcel or get_french_building_permits. The usage context is implied by the listed scope, not made explicit with alternatives or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already declare readOnly/openWorld/idempotent behavior, and the description adds meaningful context: it returns the 500 most recent records and states that the API paginates the rest, while noting the data comes from commune declarations to the ministry. This is valuable beyond the annotations, though pagination mechanics are not detailed.

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 a single dense sentence that front-loads the permit types, then lists the returned fields, data source, and pagination limit. Every clause adds information, though breaking it into shorter sentences or bullets would improve scannability.

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?

With no output schema, the description carries the full burden of explaining return content, and it lists all relevant fields (date, nature, address, parcel, housing/surface, status, applicant if legal entity). It also covers the key behavioral constraint of the 500-record limit and pagination, making the tool complete for a single-parameter read-only query.

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 single parameter code_insee is already fully documented in the schema with a pattern and an example (94081 Vitry-sur-Seine), and schema coverage is 100%. The description adds no parameter-specific detail, but that is unnecessary at this coverage level.

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 names a specific resource: building, development, demolition permits and prior declarations in a French commune, with a precise data source (Sitadel) and field list. This clearly differentiates it from the sibling tools (address facts, commune facts, parcel), which cover different data domains.

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 gives clear context for when to use the tool: any time permit/urbanism authorization data for a commune is needed, keyed by code_insee. It does not explicitly name alternatives or exclusions, but the resource is distinct enough from the siblings that no comparison is needed.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds valuable behavioral context beyond that: Paris/Marseille/Lyon resolve to arrondissements, and the output mirrors the commune page .md twin, which tells the agent what to expect. No contradiction with annotations.

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?

Three sentences front-load the fact list and add one essential caveat. The .md twin phrase is slightly cryptic but concise and informative; there is no redundant filler.

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?

With no output schema, the description carries the burden of indicating what will be returned; it lists six fact categories and the special city behavior. It is complete enough for a single-parameter read-only tool, although the exact response format/markdown structure is left vague.

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 coverage is 100% and the code_insee parameter is already documented with pattern and an example. The description does not add parameter-format detail beyond that, so the baseline 3 applies; the arrondissement note is output behavior rather than parameter syntax.

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 enumerates the exact commune-level facts returned (voies adressées, median price, urbanism document, zone A/B/C, fibre eligibility, risks) and notes the arrondissement behavior for Paris/Marseille/Lyon. It is clear enough to differentiate from address, permit, and parcel siblings, though it lacks an explicit verb like 'returns' and reads as a noun-phrase definition.

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 content list makes the intended use implicit: call this for aggregate commune facts rather than address details, permits, or parcels. However, it never explicitly states when to prefer this tool over the siblings or mentions any exclusion conditions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so no contradiction exists. The description adds useful context about which fields are returned and explicitly excludes owner data. Yet the undefined 'twin' claim is vague and may confuse rather than clarify what behavior to expect.

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 compact and front-loaded with the input format and core outputs, achieving conciseness without padding. The second sentence is slightly cryptic, especially the twin reference, but it still covers both inclusions and exclusions in a short span.

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 single-parameter tool with rich read-only annotations, the description gives the key inputs and outputs and notes the owner omission. The main gap is the unexplained 'jumeau' relationship, which leaves an agent unsure what additional data exists or where to find it, so the description is not fully self-sufficient.

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 schema alone already gives the parameter pattern and an example, achieving 100% coverage. The description adds semantic structure by decomposing the identifier into INSEE code, prefix, section, and number, which goes beyond the schema's raw pattern and aids correct construction of parcel_id.

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 retrieves a French cadastral parcel by its 14-character identifier and lists the returned fields (contenance, commune, closest road). This distinguishes it from siblings like get_french_building_permits or get_french_commune_facts, though the cryptic 'dont le jumeau porte tout le reste' weakens explicit differentiation.

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 implicitly tells the agent this is the surface-level parcel lookup and that a twin tool carries 'all the rest', and it explicitly notes the owner is not served. However, it never names the alternative tool or gives concrete conditions for when to prefer a sibling, leaving routing partially to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.