Cubi Estate

Corpus statistics

corpus_stats

Read-only

Day-by-day Cubi corpus activity — aggregate counts only, no listings.

Use this for dashboards or "how is the corpus moving" questions: active
listing totals, per-country breakdown, and per-day series of newly ingested
listings, deactivated listings, and detected price drops.

Args:
    days: Trailing window length in days (1-90, default 30).
    country: Optional full country name filter, e.g. "Portugal", "Spain".
             Case-insensitive. Omit for the whole corpus.

Returns:
    JSON string: {country, days, active_listings, active_by_country,
    daily: {new_listings, deactivated, price_drops}} where each daily
    series maps "YYYY-MM-DD" to a count (price_drops adds avg_pct,
    negative = average drop size in percent).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`days`	No
`country`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 1, 2026

TDQS

A4/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is fully covered by structured data. The description adds only that results are aggregated with no per-listing detail, which is useful but modest added behavior context.

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 and scope are front-loaded in the first sentence, followed by usage and then args/returns. The structure is clean, though the Returns block is somewhat redundant given an output schema exists.

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 a two-parameter read-only aggregate tool with annotations and an output schema, the description covers everything an agent needs: purpose, scope boundary, usage context, and full parameter semantics. The only redundancy is re-documenting the return shape already present in the output schema.

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

Parameters5/5

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

Schema coverage is 0%, so the description carries the full burden and does it well: it documents the day window range and default (1-90, default 30), and explains that country is an optional full country name, case-insensitive, with omission meaning the whole corpus. Both parameters get syntax and semantics beyond anything in the schema.

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?

The description gives a specific resource (Cubi corpus activity) and an explicit scope boundary — 'aggregate counts only, no listings' — which cleanly separates it from listing-oriented siblings like filter_listings and search_properties. It does not, however, distinguish itself from the other stats siblings (raise_stats, data_layer_stats), so the agent gets no help choosing among them.

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 states concrete use cases ('dashboards', 'how is the corpus moving' questions) and enumerates what the response covers, which is clear contextual guidance. There is no explicit when-not-to-use or named alternative, so it stops short of a 5.

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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