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mcp-job-board-keyword-signal-scanner

Scan Job Board Keywords

scan_job_board_keywords
Read-onlyIdempotent

Scan a company's job board for roles by category (GTM, Engineering, etc.) across Greenhouse, Lever, Ashby, Workday, and Rippling. Returns matched role counts and titles per category for targeting signals.

Instructions

Scan a company's job board for roles in chosen categories across Greenhouse, Lever, Ashby, Workday, and Rippling. Pick from GTM, Engineering, Finance, Operations, Executive, or supply custom keywords. Returns a flat, Clay-ready JSON row of matched role counts and titles per category. Read-only; requires an APIFY_TOKEN and consumes Apify credits per call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
company_domainYesBare company domain without https:// and without a trailing slash. Example: stripe.com
role_categoriesYesOne or more role categories to scan for. Valid values: GTM, Engineering, Finance, Operations, Executive, Custom. Use Custom together with custom_keywords.
custom_keywordsNoKeyword strings to match when Custom is included in role_categories. Required only if Custom is requested.
enable_fallbackNoIf true, falls 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, used to compute newly added or removed roles.
previous_run_dateNoISO date of the previous run, e.g. 2026-03-15. Used for tracking changes over time.
Behavior5/5

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

Description adds 'Read-only; requires an APIFY_TOKEN and consumes Apify credits per call' beyond the annotations (readOnlyHint=true). This provides critical behavioral and cost information.

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?

Three sentences front-load the key purpose, then output format, then requirements. 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.

Completeness4/5

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

Description covers return type, cost, and required token. Without output schema, it sufficiently explains outputs. Minor omission: no details on error cases, but acceptable given complexity.

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. The description adds minimal extra meaning since schema already explains parameter choices; no compensation needed.

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?

Description clearly states verb 'scan', resource 'job board', and specific ATS platforms (Greenhouse, Lever, etc.), distinguishing it from any potential siblings. It also mentions the output format.

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?

Description explicitly lists role categories to pick from and explains when to use custom keywords. While no sibling tools exist to provide usage alternatives, it gives clear guidance on usage context.

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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