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TVLSS

HireJack

Find Emerging Roles

find_emerging_roles
Read-only

Detect job roles that are gaining company adoption by comparing current hiring counts against a past window. Shows growing titles and newly detected roles to reveal what companies are suddenly hiring for.

Instructions

Job roles gaining company adoption — which titles are spreading across the market. Analyst tier. Compares each canonical role's company count today against a daily rollup snapshot windowDays ago (default 21) and returns roles that cleared the growth thresholds, plus genuinely NEW titles the classifier just started seeing (how 'Forward Deployed Engineer' was first caught). Use for 'what roles are companies suddenly hiring for?' or 'is the AI-engineer title spreading?'. Defaults: windowDays 21, minCompanies 5, minDelta 2, growthMin 25%, limit 20. Companion: find_emerging_skills for the skill-level signal.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax roles to return (default 20)
minDeltaNoMinimum ABSOLUTE company-count gain over the window (default 2). Filters small-base noise.
growthMinNoMinimum growth percentage over the window (default 25).
windowDaysNoGrowth window in days (default 21). Company adoption today is compared against the newest daily snapshot at or before this many days ago. The daily rollup began 2026-07-03 (90-day retention) — if the history is younger than the window, growth is measured over the available span and the response says so.
minCompaniesNoMinimum companies that must currently post the role (default 5).
Behavior5/5

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

Annotations already declare readOnlyHint=true, and the description expands on this by detailing the snapshot comparison, retention window behavior, and handling of short history. It fully discloses the algorithm and edge cases, exceeding annotation coverage.

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?

The description is well-structured, starting with the core purpose, then details, use cases, defaults, and companion tool. It is slightly wordy but every sentence adds value, and the information density is high without being excessive.

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?

Given the absence of an output schema, the description explains the output type (roles cleared thresholds + new titles) and the underlying algorithm. It could briefly mention the response shape, but the provided details are sufficient for confident usage.

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 100%, and the description adds significant value by explaining each parameter's role (e.g., minDelta filters small-base noise, growthMin is percentage), listing defaults, and clarifying windowDays retention behavior. This greatly enhances understanding beyond the schema.

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 it identifies job roles gaining company adoption by comparing company counts over a window. It distinguishes itself from siblings like find_emerging_skills and provides concrete use cases, making the tool's purpose unmistakable.

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?

The description explicitly mentions usage scenarios ('what roles are companies suddenly hiring for?') and defaults, and notes the companion tool. It does not explicitly state when not to use, but the context is clear enough for appropriate selection.

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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