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TVLSS

HireJack

Watchlist Intelligence

watchlist_intelligence
Read-only

Aggregate hiring intelligence for your watched companies: total jobs, hiring trend, top skills, median salary, and weekly job changes. Identify scaling companies.

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?'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Annotations indicate readOnlyHint=true and openWorldHint=false. The description confirms it is a read operation and adds behavioral details about returning aggregate data per watched company, including field descriptions. No contradictions.

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 two sentences, front-loaded with the main purpose. The second sentence lists return fields in a readable way. It could be slightly more structured, but it is efficient.

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 no parameters and no output schema, the description comprehensively explains what is returned and when to use it. It covers all necessary context for an agent to invoke the tool correctly.

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?

There are no parameters, and the description does not need to explain parameters. It provides sufficient context about what the tool returns without needing parameter semantics.

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 aggregates hiring intelligence for watched companies, lists specific return fields (totalJobs, hiringTrend, etc.), and gives example queries. This distinguishes it from sibling tools like company_fit or compare_companies.

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 mentions 'Pro tier' as a usage restriction and provides example queries for when to use it. However, it does not explicitly state when not to use it or list alternative tools.

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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