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datasets_jobs_facets

Get hiring market aggregates from open job postings. Facet by company, location, skill, seniority, remote share, and more to analyze live demand.

Instructions

Facet the jobs dataset (hiring market aggregates). Aggregations over all open postings: top companies hiring, breakdown by provider (every provider filterable via /datasets/jobs/search's provider param), department, location, employment type, skill, benefit, education, security clearance, seniority, and ESCO/ISCO job family, plus the remote share — a live hiring-market snapshot. Seniority uses one mutually exclusive value: entry, mid, or senior; ambiguous occupations are omitted from job-family buckets.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sizeNoBuckets per facet, default 20, max 100

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.6.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses the facet dimensions, aggregation over all open postings, the remote share inclusion, and the seniority mutual-exclusivity rule with omission of ambiguous occupations. This exceeds a simple purpose statement, though it does not cover response structure or rate limits.

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 a dense single paragraph that front-loads the core purpose. Each clause adds specific information (facet list, provider filter note, seniority quirk), though the lengthy list makes it slightly heavier than necessary.

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 one optional parameter and no output schema, the description adequately enumerates the returned facets and notes behavioral caveats (seniority, provider filtering via search). It does not detail the exact result format, but the facet list is sufficient for a facet tool.

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?

The only parameter `size` is fully described in the schema (default 20, max 100). The description does not mention `size`, but with 100% schema coverage, the baseline of 3 applies.

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 opens with 'Facet the jobs dataset' and enumerates specific aggregations (top companies, provider, department, location, etc.), making the action and resource clear. It distinguishes itself from sibling search/item tools by explicitly focusing on aggregates and the 'live hiring-market snapshot'.

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 says provider filtering is done via /datasets/jobs/search's `provider` parameter, implying this tool is for unfiltered aggregates while search handles filtered queries. It provides clear context as a market-level snapshot, but does not explicitly name alternative tools or give when-not-to-use guidance.

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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