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TVLSS

HireJack

Watchlist Intelligence

watchlist_intelligence
Read-only

Aggregate hiring intelligence for your watched companies: get open jobs, hiring trends, top skills, median salary, and week-over-week changes.

Instructions

Aggregate hiring intelligence on every company the authenticated user is watching. Pro tier. For each watched company returns: totalJobs currently open, hiringTrend (up/down/stable), trendPct (week-over-week %), topSkills the company is hiring for, medianSalary, sparkline data (last ~12 weeks of job counts), and jobDelta since the previous snapshot. Use for queries like 'what's happening at the companies I'm watching?' or 'which of my watched companies are scaling up right now?'. Not for a plain list of watched companies — use list_watchlist for that.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesHiring intelligence for every company on the user's watchlist
metaNoRequest/response metadata (pagination cursors, snapshot timestamps, applied filters)
citation_urlNohirejack.com URL to cite when surfacing this result
Behavior4/5

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

Annotations already indicate readOnlyHint=true; the description adds valuable context about Pro tier requirement and details the returned fields (hiringTrend, trendPct, etc.) without contradicting 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 concise (few sentences), front-loaded with purpose, then return fields, then usage guidelines. Every sentence adds value with no fluff.

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

Completeness5/5

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

Given zero parameters and an existing output schema, the description fully covers purpose, usage, return values, and constraints (Pro tier), making it complete for an AI agent.

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?

No parameters are defined, so the description correctly focuses on the output and usage. The 0-param baseline is 4, and the description adds no unnecessary param info.

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 states a specific verb ('aggregate') and resource ('hiring intelligence on watched companies'), clearly distinguishing from sibling 'list_watchlist' which returns a plain list.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly provides example queries for when to use the tool and specifically states when not to use it, naming the alternative tool (list_watchlist).

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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