Delete Product
neuron_delete_productPermanently delete a product. Existing orders are retained.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Product UUID |
neuron_delete_productPermanently delete a product. Existing orders are retained.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Product UUID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark this destructive and non-read-only. The description adds valuable behavioral nuance by stating the deletion is permanent and that existing orders are retained. This goes beyond the annotations and gives the agent important side-effect information.
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 short, front-loaded sentences with no unnecessary words. It communicates the primary action and the most important behavioral caveat 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 one-parameter delete tool with annotations already covering destructiveness and idempotency, the description is nearly complete. It adds the key nuance that orders are retained. It could go slightly further by describing expected output or failure behavior, but the tool's simplicity makes this a minor gap.
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?
The schema provides 100% coverage for the single parameter `id` with the description 'Product UUID'. The description adds no additional parameter-level meaning, but the schema already fully documents the parameter, so the baseline score of 3 is appropriate.
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 a specific verb ('delete'), resource ('product'), and key scope detail ('permanently'). It also clarifies a behavioral boundary with 'Existing orders are retained,' which helps distinguish this from other delete tools in the sibling list. An agent can clearly understand what this tool does.
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 usage is implied by the description: use this when you need to permanently delete a product. However, it does not explicitly state when not to use it or mention alternatives such as disabling a product or using an update operation. It provides clear context but no explicit exclusions or alternative routing.
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.