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Save the video & get an open link (client-render handoff)

neuron_studio_save

Persist a ProjectDoc and get back an openUrl. Opening it drops the video into the Studio in the user's browser, where 'Export MP4' renders CLIENT-SIDE (on their machine, no server render cost). Use this for the hybrid flow: build with neuron_studio_apply/agent, then save and hand the user the link. Use neuron_studio_render instead only for headless/unattended server renders.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
docYesThe ProjectDoc to save.
nameNoA title for the saved video (default 'AI video').

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate the tool is not read-only (readOnlyHint=false) and not destructive, and the description adds the key behavioral context that actual MP4 export happens client-side in the user's browser, so no server render cost is incurred. This is a meaningful trait an agent needs to know when choosing between save and render.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three purposeful sentences: the first states the core function and return, the second explains workflow context, and the third routes to the alternative. No repeated or filler content; every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description still discloses the important return value (`openUrl`) and its downstream effect (video drops into the Studio for client-side export). It also covers the primary input (`doc`), the optional name, and how to choose between this and `neuron_studio_render`, making the tool safely invocable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%: `doc` and `name` each have descriptions, including the default for `name`. The description does not add parameter-level detail beyond restating the `doc` concept, so the high schema coverage is sufficient and the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific action ('Persist a ProjectDoc') and a concrete return value (`openUrl`), and the title states the resource (video save + open link). It also distinguishes itself from `neuron_studio_render`, which is a sibling that performs server-side rendering.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly says when to use this tool: after building with `neuron_studio_apply`/`agent`, when handing the user a link in the hybrid flow. It also names the alternative `neuron_studio_render` and excludes it for all but headless/unattended server renders.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.4/5.0
Disambiguation3/5

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.

Naming Consistency4/5

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.

Tool Count1/5

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.

Completeness4/5

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.

Resources