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Scan Job Board Keywords

scan_job_board_keywords
Read-onlyIdempotent

Scan a company's job board to find roles in chosen categories across Greenhouse, Lever, Ashby, Workday, and Rippling. Returns matched role counts and titles per category as a flat JSON row.

Instructions

Scan a company's job board for roles in chosen categories across Greenhouse, Lever, Ashby, Workday, and Rippling. Returns matched role counts and titles per category as a flat, Clay-ready JSON row. Read-only; requires an APIFY_TOKEN and consumes Apify credits per call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
company_domainYesBare company domain, e.g. stripe.com
role_categoriesYesOne or more of: GTM, Engineering, Finance, Operations, Executive, Custom.
custom_keywordsNoKeyword strings to match when Custom is included in role_categories.
enable_fallbackNoFall back to a pre-indexed job database when the live ATS cascade finds nothing.
previous_roles_detectedNoComma-separated matched role titles from a previous run, to compute deltas.
previous_run_dateNoISO date of the previous run, e.g. 2026-03-15.
Behavior4/5

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

Beyond annotations (readOnlyHint, idempotentHint), description adds that it is read-only and consumes Apify credits, which is useful operational context. No contradictions 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?

Two sentences, front-loaded with purpose and supported platforms, then output format and constraints. No wasted words, highly 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?

For a tool with no output schema, description sufficiently describes return format ('flat, Clay-ready JSON row' with role counts and titles). All 6 parameters are fully described in schema, and context signals indicate high schema coverage. No gaps identified.

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%, so baseline is 3. Description summarizes the purpose of parameters ('chosen categories') but does not add significant meaning beyond what the schema already provides for each parameter.

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?

States specific verb 'scan' and resource 'job board', lists supported ATS platforms (Greenhouse, Lever, Ashby, Workday, Rippling), and describes output as 'matched role counts and titles per category as a flat, Clay-ready JSON row'. Clearly distinguishes from sibling tools like scan_gtm_hiring_signals.

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?

Explicitly states read-only nature and prerequisites: 'requires an APIFY_TOKEN and consumes Apify credits per call'. Missing explicit when-not-to-use or comparison with siblings, but context is clear enough.

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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