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moonie0201

ats-jobs-mcp

by moonie0201

search_jobs

Find current job openings at specific companies by pulling live postings from their applicant-tracking system boards. Filter by remote status and job title, and cap results to control costs.

Instructions

Fetch live job postings for named companies straight from their ATS board API. Companies are 'provider:slug' pairs (lever:palantir) or career-site URLs; use resolve_company first if you only have a name. Filters are applied before billing, so a narrow query is cheap. Billed to your own Apify account at $0.002 per returned job row: max_jobs is the hard spend cap.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
max_jobsNo
companiesYes
remote_onlyNo
title_containsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries the full burden and does a good job: it discloses billing per row, max_jobs as a hard spend cap, filter-before-billing semantics, and that data is fetched live from ATS APIs. It does not mention failure modes for invalid companies or rate limits, but the key operational trait—cost—is transparent.

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?

Four sentences, all earning their place: the core operation is front-loaded, followed by identifier format, cost behavior, and spend-cap warning. There is no filler or repetition of schema fields.

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

Completeness3/5

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

Output schema covers return values, so that omission is fine. The definition is strong on cost and company-identifier format, but because two parameters are undocumented in both the schema and description, and list_supported_ats is never referenced, the agent cannot fully plan all call variants.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It explains companies (provider:slug or career-site URL) and max_jobs (spend cap), but remote_only and title_contains are left completely unexplained, leaving the agent unable to know their exact behavior from the definition.

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 opening sentence identifies a specific action (fetch live job postings) and a concrete resource (named companies' ATS board APIs). The provider:slug/URL detail and 'use resolve_company first' clearly differentiate it from its sibling tools.

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 instructs to call resolve_company first when only a name is available, and explains that narrow queries are cheaper because filters are applied before billing. However, no guidance covers when to use list_supported_ats or when search_jobs would not be the right tool.

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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