neuron_remove_bot_channelDisconnect a WhatsApp channel from a bot. The bot will no longer respond to messages on this channel.
| Name | Required | Description | Default |
|---|---|---|---|
| botId | Yes | Unique identifier (UUID) of the bot | |
| channelId | Yes | Unique identifier (UUID) of the WhatsApp channel to disconnect |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare destructiveHint=true, so the description adds context about the bot ceasing to respond. However, it does not fully disclose additional behavioral traits like reversibility, side effects, or whether the channel is deleted. Given annotations carry part of the burden, the description adds some but not extensive 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 at two sentences. It front-loads the action in the first sentence and clarifies the consequence in the second. Every word contributes meaning, with no redundancy or unnecessary details.
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 schema, annotations, and no output schema, the description covers the core functionality and effect. It lacks prerequisites or preconditions (e.g., channel must be connected), but this is a minor gap. Overall, it provides sufficient context for an agent to invoke correctly.
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% with clear descriptions for both parameters (botId and channelId). The description does not add any additional meaning beyond what is already in the schema, so it meets the baseline without extra value.
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 action 'disconnect', identifies the resource as 'WhatsApp channel from a bot', and explains the effect: 'bot will no longer respond'. It implicitly distinguishes from sibling tools like 'unassign_bot_from_channel' by specifying the channel type (WhatsApp).
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 usage when one wants to disconnect a WhatsApp channel, but it lacks explicit guidance on when to use this tool versus alternatives (e.g., unassign_bot_from_channel for non-WhatsApp channels). No 'when not to use' or exclusion criteria are provided.
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.