neuron

Compose Message

neuron_compose_message

Compose and send a new message to a phone number via a specific channel. Automatically creates a new conversation if one does not exist.

Input Schema

TableJSON Schema

Name	Required	Description
`to`	Yes	Recipient phone number (E.164 format, e.g., '2348012345678')
`text`	Yes	Message text content
`sendAt`	No	ISO 8601 date-time for scheduled delivery (e.g., '2025-12-31T10:00:00Z'). Message sends immediately if omitted.
`mediaUrl`	No	URL of media to attach (required for non-text message types)
`channelId`	Yes	Unique identifier (UUID) of the WhatsApp channel to send through
`contactName`	No	Display name for the recipient contact
`messageType`	No	Type of message: 'text' (default), 'image', or 'document'

TDQS

B3.2/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations provide no hints (all false), so the description carries the burden. It discloses that the tool creates a new conversation if one does not exist, which is a key behavioral trait. However, it does not detail any other side effects, error conditions, or what happens if the conversation already exists.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences with no wasted words. The purpose is front-loaded in the first sentence, and the second adds a valuable detail about conversation creation. Structurally efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 7 parameters, no output schema, and many sibling tools, the description covers the basic operation and auto-conversation creation. But it omits return value, failure modes, and permission requirements, leaving some gaps for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with clear descriptions for each parameter (e.g., E.164 format for 'to', ISO 8601 for 'sendAt'). The top-level description adds minimal extra semantics beyond the schema, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it composes and sends a new message to a phone number via a specific channel, and it mentions automatic conversation creation. However, it does not explicitly differentiate from similar sibling tools like neuron_send_message or neuron_send_whatsapp, leaving some ambiguity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives. It does not mention prerequisites, such as needing an active channel, or when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.4/5.0

Disambiguation3/5

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.

Naming Consistency4/5

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.

Tool Count1/5

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.

Completeness4/5

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.

Resources

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