neuron_update_broadcastUpdate an existing broadcast's name, message content, or media before sending. The broadcast must still be in draft status.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Unique identifier (UUID) of the broadcast to update | |
| name | No | New display name or label for the broadcast | |
| message | No | New message content to broadcast | |
| mediaUrl | No | New media URL to attach | |
| messageType | No | New message type (e.g., 'text', 'image', 'document') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds the draft status constraint beyond what annotations provide. Annotations already indicate idempotentHint=true (safe to retry) and destructiveHint=false. The description does not discuss idempotency or error behavior but covers the key prerequisite.
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 no wasted words. The first sentence states the core purpose and what fields can be updated. The second adds a vital constraint. Every word earns its place.
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 tool has 5 parameters (1 required) and no output schema, the description provides the essential constraint and purpose. It could mention the return value or behavior on invalid state (like non-draft) but is still fairly complete for a simple update operation.
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%, so the schema fully documents parameters. The description restates 'name, message content, or media' but does not add deeper semantics in format, defaults, or interaction between parameters. Baseline 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 ('Update'), the resource ('broadcast'), and what can be updated ('name, message content, or media'). It also includes a constraint ('before sending') and distinguishes from sibling tools like 'create_broadcast' and 'send_broadcast'.
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 explicitly says the broadcast 'must still be in draft status,' which provides a clear when-to-use condition. It also implies not to use after sending. However, it does not mention any alternative tools for cases where the broadcast is not in draft status.
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.