droptrack_list_playlistsList all playlists in the current company with track counts.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of results per page | |
| cursor | No | Pagination cursor (offset) |
| Name | Required | Description | Default |
|---|---|---|---|
| result | No | Structured DropTrack result returned by this tool |
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already disclose that the operation is read-only and non-destructive, so the description does not need to repeat that. It adds useful scope context ('current company') and output content ('track counts'), but it does not disclose pagination behavior or any additional call-specific side effects; the schema covers pagination rather than the description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that states the action, scope, and key output field without filler. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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 complete input schema, an output schema, and safety annotations, the description is sufficient. It identifies the resource scope ('current company') and the distinguishing output ('track counts'), so an agent can choose and invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Parameter schema coverage is 100%, with clear descriptions for both 'limit' and 'cursor'. The description adds no parameter-level semantics beyond the schema, which matches the baseline of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'List' with a clear resource ('playlists in the current company') and adds the distinguishing detail 'with track counts.' This clearly separates it from related siblings such as droptrack_get_playlist_details, which would be used for a single playlist's detail view.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'List all playlists' implies this tool is the right choice for a collection-level playlist query, but it does not explicitly state when to prefer it over droptrack_get_playlist_details or how the 'current company' context is established. There is no when/when-not or alternative guidance, leaving usage inferred rather than explicit.
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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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.
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.
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.
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.