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DropTrack List Assets

droptrack_list_assets
Read-only

List company assets: uploaded/AI images or documents.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of results per page
cursorNoPagination cursor (offset)
targetIdNoOptional track or playlist ID when targetType is provided
assetTypeNoAsset category to listimage
playlistIdNoOptional playlist ID when assetType is document
sourceTypeNoOptional image source filter when assetType is image
targetTypeNoOptional image target type

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

B3.4/5.0
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 the 'company' scoping context and the asset-type taxonomy, which is moderately useful, but it does not disclose behavioral details such as default assetType=image filtering or pagination behavior beyond what the schema already states.

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?

A single front-loaded sentence that states the verb, resource, and scope with zero filler words. Every element contributes meaning, and the parenthetical efficiently disambiguates the asset categories.

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 rich structured context — 100% parameter coverage, an output schema, and read-only annotations — the description covers the essential purpose adequately. The main gap is the absence of sibling-routing context, but the conditional parameter relationships are already documented in the schema, so nothing critical for invoking the tool correctly is 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 baseline of 3 applies. The description's phrase 'uploaded/AI images or documents' loosely maps to the assetType and sourceType enums, but it adds no parameter-level detail beyond what the schema already documents for each of the 7 parameters.

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 names a specific verb ('List') and resource ('company assets'), then clarifies scope with 'uploaded/AI images or documents,' which distinguishes it from sibling list tools like droptrack_list_tracks and droptrack_list_playlists by resource category. It is clear and actionable, though it does not explicitly name a sibling it is not, so it stops short of the top score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It does not mention the many related siblings (e.g., droptrack_generate_album_art, droptrack_accept_album_art_candidate, droptrack_get_ai_job) or note when a different tool would be more appropriate, leaving all routing decisions to the agent.

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.