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JobMojito

by com.jobmojito

Get merchant credit usage

get_merchant_credit_usage
Read-onlyIdempotent

[Results] Get the merchant's credit usage.

Per-event credit-usage ledger for a merchant: every billable analytics event (interview, pre-screening, public avatar, simulation, …) that consumed credits, ordered most recent first. Scoped to your token's merchant, or a merchant_id override for admins / sub-merchant operators. The credits consumed by each event are in stats.credit_amount. Capped at 1000 records per page.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of records to return (1–1000).
offsetNoNumber of records to skip from the start of the result set.
merchant_idNoOptional merchant to scope to. Admins and sub-merchant operators only; other callers always use their token's merchant.
interview_idNoOptional interview (interview_def_set) or position (position_def_set) id to drill the credit-usage ledger down to a single interview or position. The type is detected automatically: for a position the response combines pre-screening and interview-result credits across the whole position; for an interview it returns that interview's credit events (including simulations and report translations).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesCredit-consuming analytics events for the merchant, most recent first. Each row is one billable event; the credits it consumed are in stats.credit_amount.
paginationYes

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds that results are ordered most recent first and capped at 1000 records per page, which are useful behavioral details. No contradiction 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?

The description is concise (about 5 lines), front-loaded with the main purpose, and every sentence adds value. No redundancy or fluff.

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?

Given the annotations (readOnlyHint, idempotentHint, etc.) and the presence of an output schema, the description provides sufficient context: it explains the return format (ledger with credit_amount), ordering, pagination limit, and parameter behavior. It is complete for a read-only tool.

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

Parameters5/5

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

The description adds significant meaning beyond the input schema: it explains the merchant_id scope permission, and the interview_id parameter's dual behavior (detecting interview vs. position and combining credits accordingly). Schema coverage is 100%, but the description enriches understanding.

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 clearly states the tool returns a per-event credit-usage ledger for a merchant, listing billable analytics events that consumed credits. It distinguishes from siblings like get_merchant_analytics and get_merchant_status by focusing on detailed credit consumption events rather than aggregated analytics or status.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains scoping (token's merchant or merchant_id override for admins) and drilling down via interview_id or position_id. However, it does not explicitly compare to sibling tools or state when to prefer this tool over alternatives, leaving some inference required.

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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TDQS

A3.6/5.0
Disambiguation4/5

Most tools target distinct resources and actions, with clear category prefixes like [Interviews], [Results], and [Admin]. A few pairs could be confused—create_interview vs. create_interview_from_questions and update_interview vs. set_interview_state—but the descriptions do enough to separate them.

Naming Consistency4/5

The overwhelming majority follow a consistent verb_noun pattern: create_*, get_*, list_*, update_*, generate_*. The main deviation is jobmojito_configuration, which is a noun phrase rather than an action verb, and a few longer names like request_another_interview_attempt break the clean pattern slightly.

Tool Count2/5

With 29 tools, this server is above the 25+ threshold and places a significant navigation burden on an agent. The tools are organized into coherent domains, but several admin/merchant and results tools could likely be consolidated.

Completeness3/5

The core interview lifecycle is well covered: create, read, update, list, state changes, result retrieval, and report generation. However, there are notable gaps such as no delete operations for interviews or catalogue directories, no candidate management beyond listing/registration, and no explicit result-decision tool.

Resources