droptrack_get_campaign_analyticsGet per-contact engagement analytics for a campaign — opens, plays, downloads, and feedback for each recipient.
| Name | Required | Description | Default |
|---|---|---|---|
| campaignId | Yes | The campaign 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. The description adds that results are per-recipient and include opens, plays, downloads, and feedback, but it does not disclose details such as pagination, time-window limits, or empty-result semantics. This is acceptable given the output schema exists.
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?
A single tightly worded sentence front-loads the main action and resource, then lists the exact engagement types. No words are wasted and no redundant restatement of the tool name appears.
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 one-parameter read-only tool with a full input schema and an output schema, the description is complete enough: it names the campaign context, the per-contact granularity, and the engagement dimensions returned. Nothing else is required to invoke it correctly.
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?
The only parameter, campaignId, is already fully described in the schema ('The campaign ID') with 100% schema coverage. The description adds no campaignId-specific meaning, so it meets the baseline but does not go beyond the schema.
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 states a specific verb ('Get'), a precise resource ('per-contact engagement analytics for a campaign'), and enumerates the exact metrics included (opens, plays, downloads, feedback). This clearly differentiates it from sibling tools like droptrack_get_campaign_details and droptrack_get_ad_analytics, which target different data.
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 scoping phrase 'per-contact engagement analytics ... for each recipient' implies when to use it, but there is no explicit when-not or pointer to alternatives such as get_campaign_details or get_campaign_nudge_summary. An agent must infer selection from the resource wording.
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.