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DropTrack List Ad Campaigns

droptrack_list_ad_campaigns
Read-only

List Grow/ads campaigns for the current company with budget, delivery, landing page, and creative counts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of results per page
cursorNoPagination cursor (offset)
statusNoOptional ads campaign status filter such as draft, active, live, paused, completed, or failed
promotionTypeNoOptional promotion type filter such as spotify, droptrack, or artist

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

A3.6/5.0
Behavior3/5

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

Annotations (readOnlyHint=true, destructiveHint=false) already establish this as a safe, closed-world read operation, lowering the burden on the description. The description adds moderate value by stating the scope ('current company') and the data facets returned (budget, delivery, landing page, creative counts), but it does not go deeper into return-shape quirks or filter interplay. This is consistent with the read-only annotations; 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence that fronts the action ('List') before the resource, scope, and return data, with zero filler or repetition of schema details. Every clause earns its place, making it appropriately sized and well-structured.

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?

For a simple paginated list tool with four optional, fully documented parameters, an output schema, and read-only safety annotations, the description covers the essential invocation context. The only material gap is defining the 'Grow/ads' category and explicitly distinguishing it from droptrack_list_campaigns and droptrack_list_label_ad_campaigns, which is a differentiation gap rather than a structural one.

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%: all four parameters (limit, cursor, status, promotionType) carry their own descriptions, defaults, and bounds. The description contributes no additional parameter semantics—it summarizes return fields rather than input meaning—so the baseline 3 applies.

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 pairs a specific verb ('List') with a well-defined resource ('Grow/ads campaigns for the current company') and previews key returned fields (budget, delivery, landing page, creative counts). The 'current company' scope and ads focus help differentiate it from siblings like droptrack_list_campaigns and droptrack_list_label_ad_campaigns, though those alternatives are never named. The unexplained 'Grow/ads' terminology keeps this from a 5.

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

Usage Guidelines3/5

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

The 'current company' scoping provides an implicit usage signal: this is the company-level ads campaign list, as opposed to label-level (droptrack_list_label_ad_campaigns) or general campaigns (droptrack_list_campaigns). However, there is no explicit when-to-use statement, no exclusion, and no named alternative, so the agent must infer context from scope words alone.

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.