droptrack_get_contact_nudge_contextGet CRM nudge context for one contact: active campaigns watching the contact and recent automated nudges sent for that contact.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum campaigns and sent nudges to return | |
| contactId | Yes | The contact 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 carry the safety profile (readOnlyHint=true, destructiveHint=false), so the description only needs to add behavioral context beyond that. It does clarify what 'nudge context' means operationally by enumerating the two return categories, which adds genuine value. It stops short of disclosing the recency window behind 'recent', ordering, or pagination behavior, though the presence of an output schema reduces the burden.
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 sentence with a front-loaded verb+object and a colon-delimited enumeration of the two return categories. Every word earns its place — no filler, no repetition of the title or schema content.
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 two fully documented parameters, safety annotations, and an output schema present, the description is nearly complete. The only meaningful gaps are the undefined 'recent' time window and the lack of sibling disambiguation, both minor against the structured fields carrying the rest of the burden.
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%: contactId ('The contact ID') and limit ('Maximum campaigns and sent nudges to return', with min/max/default) are both documented in the schema itself. The description adds no parameter-level detail beyond what the schema already states, so the baseline 3 applies.
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 opens with a specific verb and resource ('Get CRM nudge context for one contact') and then defines the deliverable precisely via two data components: active campaigns watching the contact and recent automated nudges sent for it. The 'for one contact' scope plus the campaign/nudge breakdown distinguishes this from campaign-scoped siblings like droptrack_get_campaign_nudge_summary and droptrack_list_campaign_nudge_contacts.
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 scope 'for one contact' implies this is the tool for single-contact nudge-context lookups, and the two enumerated data components hint at what the agent will receive. However, the description never states when to prefer this over the closely related siblings (get_campaign_nudge_summary, list_campaign_nudge_contacts) or when not to use it. With roughly 55 siblings, explicit routing guidance would materially help.
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.