neuron_studio_generate_video_statusPoll an AI video job. When status is 'succeeded', videoUrl is the result (external — re-host it before use).
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Job id from neuron_studio_generate_video. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only and idempotent behavior, so the description adds job-specific context: polling until 'succeeded' and warning that the returned videoUrl is external and must be re-hosted before use. This is beyond what the annotations provide and is directly actionable.
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?
One compact sentence conveys the action, the success condition, the result field, and an important usage caveat. It is front-loaded with 'Poll' and contains no filler.
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 single-parameter polling tool with no output schema, the description covers the essential behaviors: what to poll, when the result is ready, where the result is located, and that the external URL requires re-hosting. No critical information is missing for correct invocation.
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 single id parameter is already fully described in the schema as 'Job id from neuron_studio_generate_video' (100% coverage), and the description adds no additional parameter-level detail. The baseline of 3 is appropriate when the schema carries the parameter documentation.
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 ('Poll') and resource ('an AI video job'), and the success condition ('status is succeeded') defines what the tool returns. This clearly distinguishes it from neuron_studio_generate_video, which creates the job, and from other status-polling siblings.
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 makes the polling use-case clear: call it after starting a video generation job and inspect videoUrl on success. It does not explicitly name alternatives or exclusion cases, but the context is clear enough for correct selection.
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.