droptrack_get_campaign_nudge_summaryGet CRM nudge settings, funnel totals, stuck-contact buckets, sent nudge history, and weekly digest state for a campaign.
| 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, lowering the burden on the description. The description adds the data-scope context (which five categories are returned) but does not disclose behavioral traits such as whether the digest state may be absent for campaigns without a configured digest, or whether totals are computed live. No contradiction with annotations; the 'Get' verb aligns with the read-only hint.
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, front-loaded sentence beginning with the verb 'Get' that packs five distinct data categories into a compact, scannable list. Every word earns its place; there is no filler, repetition, or boilerplate.
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 simple read-only tool with one required parameter and an output schema present, the description adequately covers the data scope, and the description need not explain return values. The only meaningful gap is the absence of explicit differentiation from the nudge-related siblings, but overall the definition is sufficient for correct invocation.
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 the baseline is 3 even without parameter details in the description. The phrase 'for a campaign' reinforces that campaignId scopes the result, but the description adds no meaningful format, range, or constraint information beyond what the schema already states for this single required parameter.
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') with a clear resource ('campaign nudge summary') and enumerates exactly what is returned: nudge settings, funnel totals, stuck-contact buckets, sent nudge history, and weekly digest state. This level of specificity distinguishes it from siblings like droptrack_get_campaign_details and droptrack_get_campaign_analytics, which cover different data domains.
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 usage context is implied through the enumerated data categories — an agent can infer this is the aggregate nudge overview for a campaign. However, there is no explicit when-to-use guidance, no exclusions, and no routing to closely related siblings such as droptrack_list_campaign_nudge_contacts or droptrack_get_contact_nudge_context, which would clarify when per-contact detail is needed instead.
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.