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JobMojito

by com.jobmojito

Get merchant analytics

get_merchant_analytics
Read-onlyIdempotent

[Results] Get the merchant's daily event analytics.

Daily event-count time-series for a merchant over a date range (the admin-portal analytics events graph), scoped to your token's merchant (or a merchant_id override). Optionally drilled to a single interview. Capped at 1000 records per page. Note: only day/event combinations with a non-zero count are returned — any day/event pair absent from the response should be treated as a count of 0 by the caller.

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.
date_toYesEnd of the date range (inclusive), YYYY-MM-DD.
date_fromYesStart of the date range (inclusive), YYYY-MM-DD.
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 event counts down. The type is detected automatically: a position aggregates the daily counts across every interview that makes up the position; an interview filters to that single definition.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesDaily event counts for the requested range, ordered by day ascending. Only day/event combinations with a non-zero count are returned; missing combinations should be treated as 0 by the caller.
paginationYes

TDQS

A4.3/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint: false), the description adds critical behavioral details: pagination cap at 1000 records per page, only non-zero counts returned (implicit zero padding), and optional drilling to interview. This provides valuable context for an AI agent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is clear and front-loaded with purpose, but it is a single paragraph that could be slightly more structured. It is concise, but the note about zero padding could be separated for clarity.

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 the tool's complexity (6 parameters, optional drilling, pagination) and the presence of an output schema, the description adequately covers usage scope, pagination behavior, and zero-padding note. It omits explicit read-only mention, but annotations cover that.

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?

The input schema covers all six parameters with descriptions, and the description adds minimal extra meaning beyond schema. It mentions scoping and drilling, but these are already in the schema descriptions. Baseline 3 is appropriate given 100% 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 clearly states the tool retrieves daily event analytics for a merchant using specific verbs and resources ('Get the merchant's daily event analytics'). It distinguishes itself from sibling tools like get_merchant_credit_usage and get_merchant_status by focusing on event-count time-series, making the purpose unambiguous.

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 explains when to use the tool, including scoping to token merchant or merchant_id override and optional drilling to an interview. However, it does not explicitly state when not to use it or compare with alternatives, which would raise the score to 5.

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