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Hiring Trend Index

hiring-trend-index

A cross-section of hiring demand by role and geography, assembled from several job sources in one call. Partial results are marked partial and list what is missing. — $0.05/call, x402 (USDC on base).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slicesYesWhat to snapshot and track. Two forms: "company:<ats-provider>:<token>" (e.g. "company:greenhouse:gitlab", "company:ashby:ramp") pulls a company's own public job board via this Actor's own live company-hiring-radar Actor. "board:<board-name>[:<keyword>]" (e.g. "board:xing-jobs", "board:jobs-ch-swiss:marketing") pulls a sample from one of this factory's own job-board Actors — see README for which board names are live today. Every run re-checks the SAME slices and reports what changed (by job function) since the last check for each one.
watch_keyNoA name for THIS set of watches, so you can run several independent hiring-trend watches from one Actor without one overwriting another's memory of what it last saw. 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.
new_window_daysNoFor company slices, how many days back a posting still counts as "new" in the underlying company-hiring-radar signal. Has no effect on board slices.

TDQS

A3.5/5.0
Behavior3/5

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

The description discloses that partial results are marked partial and list what is missing, which is a useful behavioral trait beyond the annotations. However, it does not mention that the tool stores watch state and tracks changes over time, which is a side effect implied by readOnlyHint=false and explained only in the parameter schema. 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.

Conciseness5/5

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

The description is two sentences plus cost info, with no filler. It front-loads the core purpose and a key behavioral note, and every word adds value. Structure is clean and immediately scannable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with three parameters and no output schema, the description gives a serviceable overview but omits the tool's change-tracking behavior and the two slice types, both of which are covered in the schema. The reliance on schema details is acceptable, but the description alone would leave an agent without a full picture of statefulness and integration with company-hiring-radar.

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 each parameter (especially 'slices') has detailed descriptions covering format, examples, and behavior. The tool description adds no parameter-level meaning beyond what the schema already provides, so the baseline of 3 is appropriate.

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 clearly identifies the tool as providing a cross-section of hiring demand by role and geography, assembled from multiple job sources in one call. This gives a specific resource and scope, though it lacks an explicit verb like 'get' or 'retrieve'. It also implies differentiation from siblings by emphasizing the multi-source aggregation, but does not name an alternative.

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 when to use the tool ('in one call' suggests an aggregate view), but provides no explicit guidance on when to prefer this over sibling tools like company-hiring-radar. There are no exclusions or alternative recommendations, leaving usage context to be inferred from the multi-source framing.

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

A3.8/5.0
Disambiguation3/5

Several tools overlap in signal space: hiring-radar, hiring-trend-index, layoff-tracker, and intent-signal-aggregator all touch hiring; funding-alert vs funding-round-tracker, sanctions-screening vs sanctions-update-alert, and rollup tools vs individual checks create boundary ambiguity. However, each has a distinct output format, so descriptions help.

Naming Consistency4/5

Tool names are consistently lowercase with hyphens and descriptive noun phrases (e.g., company-hiring-radar, litigation-check), but pricing_info breaks the pattern with snake_case and a non-descriptive name.

Tool Count3/5

With 20 tools, the server feels heavy and covers a wide range of premium data services, but each tool does target a distinct data source or workflow, so it's borderline rather than excessive.

Completeness4/5

The surface covers company identity, hiring, litigation, sanctions, funding, and patents well, but lacks direct financials, ownership structure, and general news monitoring beyond funding/layoffs, leaving some sales-intelligence gaps.

Resources