neuron_studio_transform_videoRun an AI transform on a hosted video via Replicate (async): op 'bg-remove' or 'face-swap' (face-swap needs imageUrl). Returns { id, status } — poll neuron_studio_transform_video_status for videoUrl. Needs the matching model version configured.
| Name | Required | Description | Default |
|---|---|---|---|
| op | Yes | Which transform. | |
| imageUrl | No | Reference face image (face-swap only). | |
| videoUrl | Yes | Source video URL. |
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?
Beyond the annotations, the description discloses key behavioral traits: execution is async via Replicate, the result is not returned directly, the status must be polled to obtain videoUrl, and a matching model version must be configured. These details meaningfully inform the agent about side effects and prerequisites without contradicting 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 two sentences with dense, useful information: operation, async nature, supported ops, return shape, polling route, and configuration prerequisite. Every clause earns its place and the key distinction is front-loaded.
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?
With no output schema, the description compensates by stating the exact return shape and pointing to the status-polling sibling. It also covers the conditional parameter requirement and the external configuration dependency, making the tool fully invocable and followable.
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%, and the schema already documents op, imageUrl, and videoUrl. The description adds conditional semantics beyond the schema by stating that imageUrl is needed specifically for 'face-swap', helping the agent understand parameter interdependence.
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 uses a specific verb ('Run') plus a clear resource ('AI transform on a hosted video') and enumerates the two supported operations: 'bg-remove' and 'face-swap'. It also distinguishes this async submission tool from the polling sibling neuron_studio_transform_video_status.
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 call is async, returns { id, status }, and tells the agent to poll neuron_studio_transform_video_status for videoUrl. It also clarifies that face-swap requires imageUrl. It does not explicitly contrast when to use this tool versus other studio generation/render tools, but the transform-specific scope and polling instruction provide solid guidance.
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.