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MakeViral

Show a model and its prices

get_model
Read-onlyIdempotent

Show one MakeViral Create model: the shapes, sizes and lengths it makes, the photos, clips and sounds it takes, special inputs (end frame, multi-shot, elements, motion style), and the price in credits for every size and length. With resolution, durationSec and count it also gives the exact price of that choice, so you can tell the user before create_image or create_video.

Input Schema

NameRequiredDescriptionDefault
countNoOptional, how many to price
modelYesModel id from list_models
resolutionNoOptional, to price one choice: e.g. '2K' (image) or '720p' (video)
clipSecondsNoOptional, seconds of a reference clip (they add to the price)
durationSecNoOptional, video length to price
referencePhotosNoOptional, video reference photos (one model charges for more than 5)

Output Schema

NameRequiredDescriptionDefault
modelYes
quoteNo
pricesYes
accountNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, so the safety profile is covered. The description goes beyond them by disclosing conditional behavior: the base response enumerates capabilities and the full price table, while supplying resolution/durationSec/count yields an exact price for that specific choice. No return format or pagination details, but the output schema covers returns.

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

Conciseness4/5

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

Front-loaded with the core action and resource, then a second sentence explaining the pricing-mode payoff. Dense but every clause carries information; the capability enumeration is slightly list-heavy but appropriate for what the tool returns.

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?

An output schema exists, so the description needn't document return shapes, and it still summarizes them. With annotations covering safety and 100% schema coverage, nothing an agent needs to select and invoke this tool correctly is missing.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description adds value above baseline by explaining how the optional parameters combine (resolution + durationSec + count) to narrow a full price table down to one exact quote, which the per-parameter descriptions do not state.

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?

States a specific verb ('Show one MakeViral Create model') and enumerates the resource's contents: input shapes/sizes/lengths, accepted media types, special inputs, and per-size/length credit prices. This clearly distinguishes it from list_models (plural listing) and from create_image/create_video (which it precedes).

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

Usage Guidelines4/5

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

Explicitly frames the use case: get pricing so you can tell the user before calling create_image or create_video, naming both alternatives. It stops short of stating exclusions (e.g. 'don't call for a model list' or 'no need to re-fetch once priced'), so it's clear but not fully rule-based.

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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