droptrack_get_ai_jobPoll a queued AI job: press release, artist bio, or album art.
| Name | Required | Description | Default |
|---|---|---|---|
| jobId | No | Album art job ID, optional when messageId is provided | |
| jobType | Yes | AI job type to poll | |
| trackId | No | Required for press_release jobs | |
| messageId | No | Message ID returned by the generate tool |
| Name | Required | Description | Default |
|---|---|---|---|
| result | No | Structured DropTrack result returned by this tool |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
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 'queued' and 'poll' context, indicating an asynchronous job lifecycle, but does not describe polling semantics, status behavior, or rate limits. This is adequate but not richly transparent.
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 one short, front-loaded sentence that names the action, resource, and supported job types. Every word contributes, and there is no filler or redundant restating of the tool name.
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?
Given that the schema fully documents parameters and an output schema exists, the description is mostly sufficient: it identifies what is polled and the relevant job types. It is slightly incomplete because it leaves usage distinctions to inference, but the structured inputs cover invocation details.
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?
Schema description coverage is 100%, so the schema already documents all four parameters and their dependencies. The description only repeats the jobType enum values without adding new meaning, so it does not elevate the baseline score.
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 clearly states the tool's function: 'Poll a queued AI job' with a specific resource (AI jobs) and enumerates the supported job types. It does not explicitly contrast this with droptrack_list_ai_jobs or the generate_* tools, so it is clear but lacks explicit sibling differentiation.
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 description gives no guidance on when to use this tool versus listing all AI jobs or when to poll after calling a generate tool. The intended usage is only implied by the word 'poll' and by schema details like 'Message ID returned by the generate tool,' but no explicit when/when-not guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.