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DropTrack

by io.github.droptrack

DropTrack List Tracks

droptrack_list_tracks
Read-only

List tracks in the current company with play, download, and view counts. Returns paginated results and, when the library is empty, guidance for requesting an audio file or Spotify/SoundCloud link.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of results per page (1-100)
cursorNoPagination cursor (offset)

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

A4.3/5.0
Behavior4/5

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

Annotations already mark the tool as read-only and non-destructive. The description adds meaningful behavioral detail beyond those annotations: results are paginated, and if the library is empty the tool supplies guidance for requesting audio files or Spotify/SoundCloud links. This gives the agent useful expectations about non-standard empty-library behavior.

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, no filler. The primary action is front-loaded, the returned metrics are named, and the pagination plus empty-library behavior are conveyed economically. Every sentence contributes useful information.

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?

For a simple read-only list tool with a 100%-documented schema and an output schema, the description is complete. It covers the scope, the data returned, pagination behavior, and the notable empty-library edge case. No critical context appears missing.

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 both parameters (limit and cursor). The description's mention of 'paginated results' reinforces the limit/cursor semantics but does not add new parameter-level meaning beyond what the schema already states.

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 identifies a specific verb ('List'), a specific resource ('tracks'), and the scope ('in the current company'), plus the distinguishing data points (play, download, and view counts). This makes it clearly distinguishable from siblings like get_track_details or get_track_analytics by framing the operation as a list-level read.

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 usage context: it is for listing tracks at the company level, with counts and pagination. It does not explicitly name alternatives or exclusion criteria, but the listing scope and count-based output make it easy for an agent to choose this over detail- or analytics-focused siblings.

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