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DropTrack Get Track Analytics

droptrack_get_track_analytics
Read-only

Get track analytics (plays, downloads, views) over a date range. Optionally scope results to a specific track version.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toYesEnd date (YYYY-MM-DD)
fromYesStart date (YYYY-MM-DD)
trackIdYesThe track ID
versionIdNoOptional track version ID to filter analytics to a single version

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultNoStructured DropTrack result returned by this tool

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds minor behavioral context by enumerating the three metric types and the optional version scoping, but it does not disclose aggregation granularity (e.g., totals vs. daily breakdowns) or timezone/date handling. Acceptable but not rich.

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?

Two sentences with zero wasted words. The core behavior (verb, resource, metrics, date range) is front-loaded in the first sentence, and the optional scoping is cleanly appended in the second.

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?

For a simple read-only tool with full schema coverage, safety annotations, and an output schema present, the description is nearly complete. The only real gap is the absence of disambiguation from droptrack_get_track_downloads, which could otherwise cause an agent to pick the wrong tool for a downloads-specific request.

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 the schema fully documents all four parameters, including the optional versionId filter. The description's mention of 'optionally scope results to a specific track version' reinforces versionId but adds no meaning beyond the schema. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Get'), resource ('track analytics'), and the metrics covered (plays, downloads, views) over a date range. It is clear and unambiguous, though it does not explicitly differentiate itself from the similarly named sibling droptrack_get_track_downloads, relying instead on the metric enumeration to signal its aggregating nature.

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 use case is implied: call this when you need track analytics metrics aggregated over a date range, optionally filtered by version. However, there is no explicit when-to-use guidance, no when-not-to-use exclusions, and no mention of alternatives such as droptrack_get_track_downloads, which is a meaningful omission given the sibling set.

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

B3.4/5.0
Disambiguation3/5

Most tools target distinct resources and actions, but several clusters are easy to confuse: get_track_analysis vs get_track_analytics vs get_track_tags, plus analyze_audio/request_track_tagging/auto_tag_tracks overlap in the audio-analysis/tagging space. The descriptions do help separate them, so careful agents can disambiguate, but the naming alone creates real misselection risk.

Naming Consistency4/5

All tools share the droptrack_ prefix, use snake_case, and follow a verb-first noun pattern, with list for collections and get for single items. Minor inconsistencies exist—add_contact vs create_contact_list, browse vs list, auto_tag_tracks—but the overall convention is predictable and readable.

Tool Count2/5

At 55 tools this is far beyond the recommended 3-15 range and well over the 25+ threshold. Many tools are near variants of each other, especially company-level vs label-level ads, analytics, and wallet tools, inflating the surface area and making selection harder.

Completeness3/5

The set covers many domains and some workflows are complete, such as album art generation/polling/acceptance/deletion and track tagging request/poll/apply. However, core lifecycle gaps remain: no update or delete for campaigns, contacts, or contact lists, no playlist mutation tools, and AI press-release/bio workflows end at polling without a save or publish step.