neuron_refresh_contact_listTrigger a refresh of a dynamic contact list. Re-evaluates criteria rules and/or AI prompt and materializes updated members.
| Name | Required | Description | Default |
|---|---|---|---|
| idOrSlug | Yes | Unique identifier (UUID) or URL-friendly slug of the dynamic list to refresh |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool is idempotent and non-destructive. The description adds behavioral context ('Re-evaluates criteria rules and/or AI prompt and materializes updated members') but does not cover potential delays, permissions, or side effects. Given annotation coverage, the description provides moderate added value.
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 extremely concise: two sentences with no wasted words. It front-loads the action ('Trigger a refresh') and efficiently explains the process. Every sentence earns its place.
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?
Given the low complexity (1 parameter, no output schema), the description adequately explains the core functionality. However, it does not clarify what the tool returns or whether the refresh is synchronous, which leaves some ambiguity for an AI agent.
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 coverage is 100%, so the parameter 'idOrSlug' is already fully described in the schema. The tool description does not mention the parameter or add any additional semantic meaning, resulting in a baseline score.
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 clearly states the tool triggers a refresh of a dynamic contact list by re-evaluating criteria or AI prompts, distinguishing it from tools that simply view or manually edit lists. However, it does not explicitly contrast with sibling tools like 'get_contact_list_members' or 'update_contact_list', so the purpose is clear but sibling differentiation is implicit.
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 provides no guidance on when to use this tool versus alternatives, prerequisites (e.g., list must be dynamic), or scenarios where it should not be used. Agents are left to infer context from the brief text.
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 are clearly separated by resource type, but there is meaningful overlap in messaging entry points (send_message, send_whatsapp, compose_message, bot_api_send) and contact ingestion/sync tools (import_contacts, populate_contacts, sync_whatsapp_contacts). The descriptions help disambiguate, but with 309 tools an agent will frequently need to read closely to pick the right one.
The overwhelming majority of tools follow a consistent verb_noun snake_case pattern: create_*, get_*, list_*, update_*, delete_*. Minor deviations like sales_stats, lead_stats, wallet_balance, and whoami break the pattern slightly, but overall naming is highly predictable.
309 tools is an extreme count for any MCP server, even a broad platform. This creates significant cognitive load and navigation overhead for agents, and far exceeds the well-scoped 3-15 tool range where coherence is strongest.
The tool surface is remarkably comprehensive across bots, contacts, campaigns, flows, knowledge bases, personas, marketplace, wallet, and products. Minor gaps exist — lead sources lack update/delete tools, and there is no single get_task or get_webhook alongside their list/update/delete counterparts — but these are workable gaps rather than dead ends.