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Market and Regulatory Data Feeds — Zinin M2M Hub

New Listing Alert

re-new-listing-alert

Watches a property portal for a saved filter and returns only the listings that appeared since the previous check. The first run on a new filter creates the baseline and says so. — $0.05/call, x402 (USDC on base).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filterYesThe search filter to pass to the chosen portal's own Actor input — same field names as that Actor's own input schema (e.g. Krisha.kz wants {"queries":["sale:apartment:almaty"]}, Rightmove wants {"location_identifier":"REGION^87490","deal_type":"sale"}). See the README's Input table for every portal's fields. Any "max_items"/"max_pages" you set here are capped at 10/2 regardless of the portal's own limits, to keep each check fast and cheap.
portalYesWhich of our own live real-estate Actors this watch calls. Each portal's own filter fields differ — see the Input section of the README for the exact fields each one expects.krisha-kz
max_itemsNoCaps how many NEW-listing rows a single run will deliver and charge for, even if more were found.
baseline_keyNoA name for THIS watch, so you can run several independent watches (different portals and/or filters) from one Actor without one overwriting another's memory of what's already been seen. Each name is scoped to YOUR OWN Apify account. The prefilled value is only there so this Actor's own daily test run has a stable, obviously-a-test name; replace it with your own watch name.

TDQS

A4/5.0
Behavior4/5

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

The description discloses the stateful behavior of the tool: 'The first run on a new filter creates the baseline and says so,' which is critical for understanding its operation. It also adds pricing context ($0.05/call, x402 on base) that annotations do not cover. Annotations indicate non-read-only and non-idempotent behavior, and the description complements this without contradiction.

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 composed of only two sentences, front-loading the core purpose and adding baseline and cost details efficiently. Every sentence contributes value, and there is no redundant or filler content.

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?

The tool has a nested filter object and no output schema, but the description explains the core behavioral contract: it returns only new listings since the previous check and handles baseline creation. It does not detail the output structure or how to reset the baseline, but the schema's baseline_key description addresses watch management, making it adequate for moderate complexity.

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?

All four parameters (filter, portal, max_items, baseline_key) have detailed descriptions in the input schema, achieving 100% schema coverage. The tool description does not add additional parameter-level meaning beyond what the schema already provides, so it relies on the schema for parameter understanding.

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 clearly states the tool watches a property portal for a saved filter and returns only listings that appeared since the previous check, giving it a specific verb and resource. This distinguishes it from sibling portal-specific scrapers (e.g., krisha-kz, rightmove-london) by emphasizing its monitoring/alert role. The first-run baseline behavior further clarifies its purpose.

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 implies periodic monitoring ('since the previous check') and mentions the first run creates a baseline, but it does not explicitly state when to use this tool over the underlying portal scrapers or other alert tools. No alternatives or exclusions are provided, so usage guidance remains implicit rather than explicit.

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

B3.4/5.0
Disambiguation2/5

Many tools have overlapping purposes, with over a dozen real-estate scrapers and half a dozen job boards differentiated only by geography. While descriptions are clear, an agent would struggle to pick the correct tool without prior knowledge of the specific site or region, leading to frequent misselection.

Naming Consistency2/5

Tool names use a mix of lowercase-hyphenated (boss-az, clinical-trials-monitor), underscore (pricing_info), and long descriptive phrases (official-gazette-regulatory-action-router). No consistent verb_noun pattern exists; some start with source domains, others with action nouns. This lack of predictability makes navigation confusing.

Tool Count3/5

36 tools is on the heavy side for a server that could have been more focused. While a 'data hub' can justify many endpoints, the high number of near-identical scrapers (12+ real estate, 6+ job boards) suggests bloat rather than well-scoped functionality. A leaner set with parameterized regional filters would be more appropriate.

Completeness2/5

The claimed domain 'Market and Regulatory Data Feeds' is poorly served: there are no stock/forex/commodity price feeds, few global regulatory sources (only FDA, SEC, EU tenders), and many tools are for job and property listings which are tangential. The set feels like a random aggregation rather than a coherent surface, with obvious gaps for core market and regulatory data.

Resources