neuron_set_group_join_approvalEnable or disable join approval mode for a WhatsApp group. When enabled, join requests must be manually approved by admins. Requires admin.
| Name | Required | Description | Default |
|---|---|---|---|
| enabled | Yes | true to require admin approval for joins, false to allow direct joins | |
| groupJid | Yes | Group JID (ending in @g.us) | |
| channelId | Yes | WhatsApp channel identifier (UUID) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (idempotent, non-destructive), the description adds context about the admin authorization requirement and the behavioral effect ('join requests must be manually approved by admins'). This helps the agent understand the tool's implications.
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 three short sentences (about 30 words) with no wasted words. It front-loads the core purpose and adds necessary context efficiently.
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 boolean toggle tool, the description covers the main functionality, effect, and prerequisite. It lacks information about return values or error cases, but this is acceptable given the low complexity and lack of output schema.
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 baseline is 3. The description indirectly clarifies the 'enabled' parameter by explaining the mode, but does not provide additional meaning for 'groupJid' or 'channelId' beyond the schema descriptions.
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 verb (enable/disable) and the resource (join approval mode for a WhatsApp group). It explicitly mentions the admin requirement, which distinguishes it from other group management tools like neuron_handle_join_requests or neuron_get_group_invite_link.
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 implies when to use the tool (to toggle approval mode) and clearly states the prerequisite ('Requires admin'). However, it does not explicitly mention when not to use it or provide alternatives, such as using neuron_handle_join_requests for managing pending requests.
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.