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DropTrack Get Ad Analytics

droptrack_get_ad_analytics
Read-only

Get Grow/ads daily analytics over a date range. Returns company totals or a single campaign when campaignId is supplied.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toYesEnd date (YYYY-MM-DD)
fromYesStart date (YYYY-MM-DD)
campaignIdNoOptional ads campaign ID

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

A4.4/5.0
Behavior4/5

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

Annotations already declare this read-only and non-destructive. The description adds useful behavioral context beyond those hints: it returns aggregate totals over a date range and switches granularity when campaignId is supplied. There is no contradiction with the readOnlyHint.

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?

Two short sentences front-load the core operation and then add the only conditional that changes behavior. There is no filler and no redundant restating of schema fields or annotations.

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?

With an output schema present and read-only/non-destructive hints in the annotations, the description covers the essential invocation context: date range, aggregation scope, and the single-campaign override. It is complete for a simple analytics getter, though it does not explicitly distinguish itself from nearby analytics siblings such as droptrack_get_campaign_analytics.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already documents all three parameters with descriptions, which sets a baseline of 3. The description adds semantic value by explaining that supplying campaignId changes the result from company totals to a single campaign, and 'over a date range' clarifies the role of from and to.

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 opens with a specific verb and resource ('Get Grow/ads daily analytics') and then states the exact scope: company totals or a single campaign when campaignId is supplied. This clearly differentiates it from sibling tools like droptrack_get_label_ad_analytics, which target label-level data.

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 provides clear context for when to use the tool: for daily analytics over a date range, optionally scoped to one campaign via campaignId. It does not explicitly name alternatives or state when not to use it, so it stops short of full routing guidance, but the intended usage is unambiguous.

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.