Cubi Estate

Data-layer coverage

data_layer_stats

Read-only

Live coverage of Cubi's per-listing enrichment data layers.

Use for a data-pipeline dashboard: how many active listings carry each
environmental / accessibility layer, the flood-risk band split, and the
per-country share of listings in a modelled High (1-in-20-year) river
floodplain.

Returns:
    JSON string. Warm cache (exact, ~6h fresh): {active_listings, cached:
    true, computed_at, coverage:{noise, air_quality, greenery, coastal,
    flood, drive_time, campsite}, flood:{high, medium, low, none, at_risk},
    flood_by_country:[{country, n, high_pct}], climate_regions, str_areas,
    price_moves}. Before the first cache tick, a fast planner-stat fallback:
    {active_listings, approx: true, cached: false, coverage:{…}}.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 1, 2026

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true/openWorldHint=false, so safety is covered. The description adds real value beyond that: it discloses cache semantics (warm cache exact, ~6h fresh) and a degraded planner-stat fallback (approx:true, cached:false) before the first tick, which is exactly the kind of behavioral nuance annotations cannot convey.

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?

Purpose is front-loaded in the first line, and the cache/fallback note is compact and earned. The full return-shape block is partly redundant given an output schema exists, so it costs a little length without full payoff.

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?

With an output schema present, the description needn't re-explain returns; its added value is the cache-freshness and fallback behavior, which it covers. Only the absence of sibling routing keeps it from being fully complete.

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

Parameters4/5

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

Zero parameters, so there is nothing for the description to compensate for; baseline 4 applies. No syntax or argument meaning is needed.

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

Purpose4/5

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

Names a specific resource (per-listing enrichment data-layer coverage) and enumerates the concrete dimensions it reports (environmental/accessibility layers, flood band split, per-country high floodplain share). This distinguishes it from siblings like corpus_stats and raise_stats, though it never states that differentiation explicitly.

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?

'Use for a data-pipeline dashboard' gives one implied context, but there is no guidance on when to prefer this over corpus_stats or raise_stats, and no exclusions. Usage is inferable from the content described but not directed.

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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