droptrack_list_label_teamList users with access to the label parent and artist sub-accounts. Requires label-admin access.
| Name | Required | Description | Default |
|---|---|---|---|
| labelCompanyId | No | Admin-only override: label company ID |
| 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, and the description is fully consistent with them. The description adds one meaningful piece of behavioral context beyond the annotations: the label-admin permission requirement, which matters for an agent deciding whether it is allowed to invoke the tool. No contradiction.
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?
Two sentences with zero filler. The action and scope are front-loaded in the first sentence, and the access prerequisite is placed in the second. 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 read-only tool with one optional parameter and an existing output schema, the description is effectively complete: purpose, scope, and access requirement are all stated, and return values are covered by the output schema. A minor gap is that it doesn't hint at whether team members are returned with roles or permissions, but that is downstream of the output schema.
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%: the single optional parameter labelCompanyId already carries the description 'Admin-only override: label company ID.' The tool description adds no parameter-level meaning beyond what the schema provides, so the high-coverage baseline of 3 applies.
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 a specific verb ('List') with a well-defined resource: 'users with access to the label parent and artist sub-accounts.' The scope phrasing implicitly differentiates it from siblings like droptrack_list_label_artists (artists, not access-granted users) and droptrack_list_label_contacts (contacts, not team access). It stops short of 5 because the sibling differentiation is implicit rather than explicit.
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 only usage guidance is the prerequisite 'Requires label-admin access,' which usefully tells the agent who is authorized to call it. However, there is no when-to-use vs. alternative guidance — nothing distinguishes this from droptrack_list_label_artists, droptrack_list_label_contacts, or droptrack_list_campaigns, which an agent could plausibly confuse with it.
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.