neuron_get_business_profileFetch the WhatsApp Business profile of an external contact, including description, website, category, and business hours.
| Name | Required | Description | Default |
|---|---|---|---|
| jid | Yes | Contact JID or phone number to look up (e.g. '2348012345678' or '2348012345678@s.whatsapp.net') | |
| channelId | Yes | WhatsApp channel identifier (UUID) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare read-only, idempotent, non-destructive behavior. The description adds value by specifying the fields included (description, website, category, business hours), which goes beyond the annotations. No contradictions.
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 a single, front-loaded sentence that conveys all essential information without any wasted words. Every part serves a purpose.
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 simplicity of the tool (two parameters, no output schema) and the annotations covering safety, the description provides sufficient context. It lists the data returned (fields). However, it could be slightly more precise about whether additional fields beyond those listed may be returned.
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 input schema covers 100% of parameter descriptions (jid and channelId). The tool description does not add any additional parameter information beyond what the schema provides, 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 clearly states the action ('fetch'), the resource ('WhatsApp Business profile of an external contact'), and lists the included fields (description, website, category, business hours). This distinguishes it from related tools like neuron_get_profile (own profile) and neuron_get_contact (basic info).
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 fetch business-specific profile data of an external contact) but does not explicitly state when not to use it or mention alternatives. Given the sibling list includes many get tools, a brief exclusion would improve clarity.
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.