droptrack_get_ads_walletGet the current company ads wallet balance and recent wallet transactions.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of transactions per page | |
| cursor | No | Pagination cursor (offset) |
| 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 clear. The description adds minimal behavioral context like 'current' and 'recent', but does not disclose pagination behavior or any other operational details. Since annotations cover the key safety traits, a mid-range score is appropriate.
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 a single, front-loaded sentence that states the action and the returned data without any fluff or repetition of the tool title. Every word earns its place, making it easy to scan and understand quickly.
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?
The output schema provides return-value details, and annotations cover the read-only safety profile. The description is sufficient for a simple, parameter-less invocation. The only minor gap is not explicitly pointing to the label-wallet sibling as an alternative, but this does not hinder correct basic use.
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?
Both parameters (limit and cursor) are fully described in the input schema with defaults, ranges, and descriptions, giving 100% schema coverage. The description does not add any parameter-specific meaning, which is acceptable since the schema already carries the full burden. This matches the baseline score of 3.
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 ('Get') and clearly identifies the resource: the company ads wallet balance and recent wallet transactions. The word 'company' distinguishes it from the sibling tool 'droptrack_get_label_ads_wallet', which targets a different scope. This is unambiguous and directly describes what the tool does.
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 when to use the tool: when you need the company ads wallet balance or transactions. However, it does not explicitly state when not to use it or mention the alternative label-wallet tool. The agent must infer the distinction from the word 'company', which is acceptable but not explicit guidance.
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.