neuron_studio_upload_mediaUpload a local image/video (base64) to the Studio media store (uncapped) so the renderer can fetch it. Returns { url, filename }. Put the url on an image/video layer.
| Name | Required | Description | Default |
|---|---|---|---|
| base64 | Yes | Base64-encoded file bytes (no data: prefix). | |
| filename | Yes | Original filename incl. extension (e.g. clip.mp4). | |
| mimetype | Yes | MIME type, e.g. video/mp4 or image/png. |
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 reveals the operation's effect (creating a reusable media-store object), the uncapped storage property, the return shape, and the intended follow-up action. It adds useful context beyond the annotations, which only indicate that the call is not read-only. It does not mention overwrite behavior, but that is not necessary for correct invocation.
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?
Two sentences carry the essential information with no filler. The action and destination are front-loaded, followed by the return contract and the practical next step, all without duplicating schema details.
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 though no output schema is provided, the description explicitly states the return contract ({ url, filename }), the destination, and exactly how to use the result. For a simple three-parameter upload tool, an agent has everything needed to call it correctly and integrate the output.
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?
All three parameters are already fully documented in the input schema with clear descriptions for base64 format, filename with extension, and mimetype. The tool description provides no additional parameter-level semantics beyond restating that the upload is base64 image/video content, so the baseline score of 3 applies.
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 precise action: upload a local base64 image/video to the Studio media store. It specifies the destination, the storage characteristic (uncapped), the consumer (renderer), and the downstream usage, which clearly distinguishes it from generic media-upload and URL-import 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?
It gives clear context: the upload exists so the renderer can fetch the media, and the returned URL should be placed on an image/video layer. It does not explicitly name alternatives such as studio_import_media_url for remote assets, but the 'local image/video (base64)' wording implies the boundary, so it earns a 4 rather than 5.
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.