droptrack_get_label_contact_360Get one contact cross-roster engagement timeline and per-artist breakdown. Requires label-admin access.
| Name | Required | Description | Default |
|---|---|---|---|
| contactId | Yes | The contact ID | |
| 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, so the safe read-only nature is established. The description adds valuable context by disclosing the admin-access requirement and clarifying the returned data shape ('engagement timeline and per-artist breakdown'), which goes beyond the annotations.
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 two short sentences with no filler. The core function is front-loaded, and the access requirement is stated succinctly. 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 single-contact lookup with a full output schema and clear annotations, the description is nearly complete. It conveys what the tool returns and who may use it. It could slightly improve by naming a sibling alternative for basic contact details, but the output schema covers return structure and no critical calling context is missing.
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 both contactId and labelCompanyId are already documented in the schema. The description adds no additional parameter-level meaning, which is acceptable given the complete schema coverage, so the baseline of 3 is appropriate.
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 a specific verb ('Get'), a specific resource ('one contact'), and a distinctive scope ('cross-roster engagement timeline and per-artist breakdown'). This differentiates it from sibling tools like droptrack_get_contact_details or droptrack_list_label_contacts, which would not provide the 360-degree engagement 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 description implies the tool is for label administrators needing a comprehensive single-contact engagement view, and it explicitly notes the 'label-admin access' prerequisite. However, it does not explicitly state when to prefer this over alternatives like droptrack_get_contact_details or when not to use 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.