neuron_cancel_approvalCancel a still-pending approval (e.g. the upstream action was aborted). Fires the approval.cancelled callback. Requires a bot API key.
| Name | Required | Description | Default |
|---|---|---|---|
| apiKey | Yes | Bot API key with 'nrn_' prefix for authentication | |
| reason | No | Optional cancellation reason | |
| approvalId | Yes | The approval request id (UUID) to cancel |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare destructiveHint=true and idempotentHint=true. The description adds useful behavioral context beyond those: it fires the approval.cancelled callback and only acts on still-pending approvals. This gives the agent a clearer model of side effects and preconditions.
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 two sentences, with the core action front-loaded and supporting details kept brief. The parenthetical example is short and valuable. No redundant or extraneous wording.
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 cancellation tool with comprehensive annotations and full schema coverage, the description is complete. It covers purpose, when to use, side effects, and authentication, without needing to explain return values (no output schema). The agent has enough to use the tool 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 apiKey and approvalId. The description does not add new parameter semantics; it only repeats the need for a bot API key. Baseline 3 is appropriate when the schema carries the parameter documentation.
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 states 'Cancel a still-pending approval' with a specific verb, resource, and scope. It also provides an example ('upstream action was aborted') and clearly distinguishes itself from approval request/response tools in the sibling list by focusing on cancellation.
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?
It gives a clear context for when to use the tool: to cancel an approval that is still pending, such as when the original action was aborted. It also notes the requirement for a bot API key. However, it does not explicitly name alternative tools or exclusion criteria, so it stops short of full guidance.
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.