neuron_studio_import_media_urlFetch a video from a direct link or a platform page (YouTube / Instagram / TikTok / X / LinkedIn) and re-host it so the Studio + renderer can use it. Returns { url, filename, kind, w, h, durationMs }. Put the returned url on a video layer in the ProjectDoc.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The source video URL or platform page. |
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 discloses that the tool fetches media, re-hosts it, and returns a structured result, which adds meaning beyond the annotations that already indicate a non-read-only, non-idempotent operation. It also tells the agent how to use the returned URL, though it does not cover failure modes or size/duration limits.
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 three tight sentences with no filler. It front-loads the action and supported sources, then gives the return contract and the next step, making it easy to scan.
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 one-parameter tool with no output schema, the description covers the input type, the return shape, and the downstream usage on a video layer. It is slightly incomplete on edge cases like unsupported sources or failure behavior, but overall sufficient 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?
Schema coverage is 100%, but the description adds practical meaning by clarifying that the url can be a direct link or a platform page and by listing YouTube, Instagram, TikTok, X, and LinkedIn. This helps the agent assess what kinds of URLs are valid.
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 (fetch and re-host) on a specific resource (video from a direct URL or platform page) and names the supported platforms. It clearly differentiates this URL-based Studio import from local upload or generation 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 usage context is explicit: use this when you have a direct video link or a platform page and need the media re-hosted for the Studio + renderer. It does not explicitly name alternatives like neuron_studio_upload_media or state when not to use this tool, so it stops short of full exclusion 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.