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DropTrack Accept Album Art Candidate

droptrack_accept_album_art_candidate
Destructive

Save an AI album artwork candidate from droptrack_get_ai_job as a reusable DropTrack image and optionally assign it to the target track or playlist.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobIdYesAlbum art generation job ID
assignNoWhether to assign the saved image to the target immediately
targetIdYesTrack ID or playlist ID
targetTypeNoTarget type that will receive the saved imagetrack
candidateIdYesCandidate ID returned by droptrack_get_ai_job for an album_art job

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

A4/5.0
Behavior3/5

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

Annotations already declare destructiveHint=true and readOnlyHint=false, so the mutation risk is known. The description adds value by explaining the two-step behavior (save image, optionally assign to target), but it does not disclose whether assignment overwrites existing artwork or whether deletion is possible later.

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?

The description is a single sentence that packs the source, action, output, and optional assignment without fluff. It is efficiently front-loaded and every word contributes to understanding the tool's operation.

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 presence of a full input schema with 100% coverage and an output schema, the description covers the workflow context well. It lacks only minor guidance on prerequisites such as job completion status, but the explicit reference to droptrack_get_ai_job makes the intent clear enough.

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%, and each parameter is already explained in the input schema. The description merely restates the target type without adding new semantic details about jobId, candidateId, assign, or targetType.

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 uses a specific verb ('Save') and names the exact resource (an AI album artwork candidate from droptrack_get_ai_job), the resulting artifact (a reusable DropTrack image), and the optional target (track or playlist). This clearly distinguishes it from siblings like droptrack_generate_album_art and droptrack_delete_album_art_image.

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 clearly implies the usage context: after obtaining candidates via droptrack_get_ai_job, this tool accepts one. It does not explicitly mention when not to use it or name alternatives, so it misses the full 'when-not/alternatives' bar.

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.