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DropTrack List Campaign Nudge Contacts

droptrack_list_campaign_nudge_contacts
Read-only

List recipients in a campaign with CRM nudge signal state, sent nudges, next scheduled nudge, and reply suppression status.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of contacts per page
cursorNoPagination cursor (offset)
searchNoOptional search term for contact name, email, or company
campaignIdYesThe 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

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds context about the fields returned, but it does not disclose any behavioral traits such as whether only nudge-enabled recipients are listed, how pagination behaves beyond schema defaults, or any access/authorization prerequisites.

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?

The description is a single, information-dense sentence with no filler. The core listing action and the returned data categories are front-loaded, and every clause earns its place by clarifying what the tool does.

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?

Given the rich annotations, 100% schema parameter coverage, and the existence of an output schema, the description is largely sufficient for an agent to invoke the tool correctly. A minor gap is that it does not clarify whether the tool returns every campaign recipient or only those with active nudge signals, though the wording leans toward listing recipients with their associated state.

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%, so all four parameters are already documented clearly with types, defaults, and constraints. The description does not add further semantic meaning to campaignId, limit, cursor, or search beyond what the schema provides, so the baseline score of 3 is appropriate.

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 uses a specific verb ('List') and resource ('recipients in a campaign') and enumerates the exact data elements included: CRM nudge signal state, sent nudges, next scheduled nudge, and reply suppression status. This clearly distinguishes it from sibling tools like get_campaign_nudge_summary or get_contact_nudge_context.

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 description implies the use case: retrieving per-recipient nudge details for a campaign. However, it does not explicitly state when to prefer this tool over alternatives like droptrack_get_campaign_nudge_summary or droptrack_get_contact_nudge_context, and it offers no exclusion criteria or when-not-to-use guidance.

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.