neuron_studio_generate_videoText → video via Replicate (async). Returns { id, status } — poll neuron_studio_generate_video_status until 'succeeded' to get videoUrl, then re-host + add_video. Needs a genvideo model version configured.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | What the clip should show. | |
| imageUrl | No | Optional starting image (image→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?
The description clearly discloses the async behavior, the immediate return shape, and the need to poll for the final videoUrl. It also surfaces a configuration prerequisite, adding useful context beyond the 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 compact and front-loaded with the core purpose, then efficiently covers the async workflow, return value, polling path, follow-up steps, and prerequisite. Every sentence contributes useful information.
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?
Even without an output schema, the description states the return shape, the polling mechanism, the follow-up actions, and the required configuration. For an async generation starter tool, this is complete enough for an agent to invoke and handle the flow correctly.
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 description coverage is 100%, so the schema already documents both prompt and imageUrl. The description adds only a small semantic hint that the prompt is the text input for the text-to-video generation, which is helpful but not essential.
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 action and resource: it generates a video from text via Replicate. It also clarifies this is the async start step, distinguishing it from the sibling status-polling tool and from image generation.
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 gives clear operational guidance: poll neuron_studio_generate_video_status until 'succeeded', then re-host and add the video. It names the prerequisite of a configured genvideo model version, but it does not explicitly say when to avoid this tool in favor of a sibling like transform_video.
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.