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niragmehta
by niragmehta

Prepare many applications at once

prepare_batch

Prepares application packets for queued jobs matching your filters, generating local drafts and a manifest for review. Nothing is submitted; entries needing human input are excluded.

Instructions

Selects queued jobs matching a filter and prepares an application packet for each, using the same drafting and validation as the single-job path. Creates local drafts and a batch manifest; nothing is sent. Applications whose questions still need a human decision are marked needs_human and excluded from batch approval. The campaign's submission.maxBatchSize is a hard ceiling, so a larger limit is silently reduced to it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
tiersNoQuality tiers to include. Defaults to A and B.
minScoreNo
trackIdsNoCampaign track ids to include.
companiesNo
locationClassesNo
minCompensationNoMinimum annualized top-of-range pay in the campaign currency.
allowUnknownCompensationNoInclude postings that publish no pay. Default false.
Behavior5/5

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

It discloses that it creates local drafts and a batch manifest, that nothing is sent, that needs_human applications are excluded from batch approval, and that maxBatchSize caps the limit. These details go beyond the readOnlyHint=false/openWorldHint=true annotations, providing crucial behavioral context.

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, each carrying key information: the action, the local-only nature, and two important edge-case behaviors. No filler.

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 8 optional parameters and no output schema, the description covers the core behavior and important constraints (local drafts, needs_human exclusion, maxBatchSize cap). However, it lacks any mention of the return value and does not elaborate on the filter parameters, which are partially documented in the schema.

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?

The description only mentions the limit parameter indirectly via the maxBatchSize cap. It does not explain the meaning of tiers, minScore, trackIds, companies, locationClasses, minCompensation, or allowUnknownCompensation, leaving half the schema descriptions to cover them. This fails to compensate for the 50% schema coverage.

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 states it selects queued jobs matching a filter and prepares an application packet for each, using the same drafting and validation as the single-job path. This clearly identifies the action (prepare batch), the resource (application packets), and distinguishes it from single-job tooling.

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 implies batch use by mentioning 'Selects queued jobs matching a filter' and explicitly notes 'nothing is sent', distinguishing it from submission tools. However, it does not explicitly name alternatives like prepare_application or preview_batch, so while context is clear, exclusions are not fully stated.

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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