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DropTrack

by io.github.droptrack

DropTrack Get Label Analytics

droptrack_get_label_analytics
Read-only

Get cross-roster label analytics over a date range: totals, artist rollups, top tracks, and top contacts. Requires label-admin access.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toYesEnd date (YYYY-MM-DD)
fromYesStart date (YYYY-MM-DD)
artistIdsNoOptional comma-separated artist company IDs; omit or use all for all artists
labelCompanyIdNoAdmin-only override: label company ID

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

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false. The description adds value beyond this by disclosing the label-admin access requirement and the cross-roster aggregation scope. There is no contradiction between the description and the 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?

Two sentences with no filler: the core operation and scope are front-loaded, followed by the useful access requirement. Every clause earns its place.

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?

The description is complete enough for selection and invocation: it states the purpose, scope, and access requirement, while the 100% parameter coverage and output schema cover parameter details and return shape. It does not discuss alternatives in depth, but that is a minor gap rather than a blocker.

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 baseline is 3 even without parameter detail in the description. The description adds contextual framing around date-range analytics, but artistIds and labelCompanyId semantics are already fully documented in the schema.

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 identifies the operation with a specific verb and resource: 'Get cross-roster label analytics over a date range,' and enumerates the contents (totals, artist rollups, top tracks, top contacts). The 'label' and 'cross-roster' framing distinguishes it from the many sibling analytics tools focused on tracks, campaigns, or ads.

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 gives clear context for when to use it: label-admin-level analytics aggregated across a roster over a date range, and it states the admin access prerequisite. However, it does not explicitly name alternative analytics tools or state when not to use this one, so it stops short of full exclusion guidance.

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.

Resources