neuron_submit_ad_requestSubmit a draft ad request for admin review. The request must be in draft status. After submission, an admin must approve it before it can be funded.
| Name | Required | Description | Default |
|---|---|---|---|
| adRequestId | Yes | Unique identifier (UUID) of the draft ad request to submit |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide no special hints (all false), so the description carries the burden. It explains that submission is a mutating operation that transitions the ad request to a pending approval state, requiring admin action before funding. This adds meaningful behavioral context beyond the raw annotations.
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, front-loads the purpose, and contains no extraneous information. Every sentence is essential and 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 no output schema, the description explains the workflow (draft -> submitted -> admin approval -> funding). It covers the preconditions and next steps, though it could mention the return value or confirmation. Still, it is largely complete for a submission tool.
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 the parameter description already explaining the UUID of the draft ad request. The tool description does not add additional parameter semantics beyond what the schema provides, so 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 ('Submit') and the resource ('draft ad request'), and distinguishes it from related tools like create, update, delete by specifying the workflow context (draft -> admin review -> funding).
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 states the prerequisite ('must be in draft status') and outlines the post-submission process (admin approval). While it doesn't name alternative tools, the context is clear enough for an agent to infer when to use this tool.
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.