VidGuy

Generate snacks

generate_snacks

Destructive

Kick off an ASYNC snack generation job for a brand. SPENDS CREDITS (18–26 — call estimate_snack_cost first). Requires a brandId: call list_brands first, or create_brand if the brand doesn't exist yet. Returns immediately with a jobId; then poll get_job. GENERATION TAKES AT LEAST ~2 MINUTES (typically 2–3 min) — set the user's expectations and don't poll as if it'll be instant. Pass render=true if you want viewable MP4/PNG assets shown inline in chat; without render the result is a layered manifest you open via the dashboard viewUrl. Credits are refunded automatically if generation fails.

Input Schema

Name	Required	Description
`name`	No
`render`	No	Also render PNGs/MP4s server-side (+3 credits)
`brandId`	Yes	Brand UUID to generate snacks for
`exclude`	No	Snack types to skip; excluding 'ai-ugc' saves 7 credits
`callbackUrl`	No	Reserved for forthcoming webhook delivery
`influencerId`	No	Influencer Vault UUID to source person-facing assets from. The influencer must belong to your account and either be linked to this brand or be account-wide. Its character sheet becomes the AI-UGC actor, and its static stills / swapped b-roll feed slides and text-wall.
`influencerMode`	No	How to use the vault when influencerId is set. 'prefer' (default) uses vault assets first and falls back to the global library per slot; 'require' refuses global fallback for influencer-aware slots (a slot may be skipped if the vault can't fill it). Ignored when influencerId is absent.

Output Schema

Name	Required	Description	Default
`jobId`	Yes
`render`	No
`status`	Yes
`exclude`	No
`pollUrl`	Yes
`offeringId`	No
`influencerId`	No
`influencerMode`	No
`creditsReserved`	Yes
`snackTypeSequence`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 13, 2026

changedInput schema / properties / exclude / description

Previous value: -"Snack types to skip; excluding 'ai-ugc' saves 5 credits"New value: +"Snack types to skip; excluding 'ai-ugc' saves 7 credits"

Changed1 schema field changedOct 1, 2026

changedInput schema / properties / exclude / items / enum

Previous value: -[
-  "slides",
-  "text-wall",
-  "ai-ugc",
-  "broll-demo",
-  "greenscreen-meme",
-  "meme-edit",
-  "single-image",
-  "sticker-pack",
-  "static-ad",
-  "ugc-ad",
-  "animated-ad",
-  "greenscreen-ad"
-]New value: +[
+  "slides",
+  "text-wall",
+  "ai-ugc",
+  "broll-demo",
+  "greenscreen-meme",
+  "meme-edit",
+  "single-image",
+  "sticker-pack",
+  "static-ad",
+  "ugc-ad",
+  "animated-ad",
+  "greenscreen-ad",
+  "music-ad"
+]

Changed1 schema field changedSep 29, 2026

changedInput schema / properties / exclude / items / enum

Previous value: -[
-  "slides",
-  "text-wall",
-  "ai-ugc",
-  "broll-demo",
-  "greenscreen-meme",
-  "meme-edit",
-  "single-image",
-  "sticker-pack"
-]New value: +[
+  "slides",
+  "text-wall",
+  "ai-ugc",
+  "broll-demo",
+  "greenscreen-meme",
+  "meme-edit",
+  "single-image",
+  "sticker-pack",
+  "static-ad",
+  "ugc-ad",
+  "animated-ad",
+  "greenscreen-ad"
+]

First observedSep 14, 2026

TDQS

A4.7/5.0

Behavior4/5

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

Annotations already declare it mutating, non-idempotent, and destructive, but the description adds substantial value beyond them: credit cost range (18–26), automatic refund on failure, async jobId return, and the semantic difference between render=true and a layered manifest. Strong disclosure; slightly short of 5 only because it doesn't clarify what 'destructive' means here (overwrite/replace behavior).

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?

Dense but front-loaded with the highest-priority facts (async, spends credits, needs brandId), and each subsequent sentence carries a distinct operational detail: timing, render, refunds. No filler sentences.

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 an output schema present, return values need not be re-described, yet the description still covers the async workflow, cost, prerequisites, and failure handling. Nothing an agent needs to invoke and follow up on 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 86%, so the baseline is 3, and the description earns above that by explaining the credit impact of parameters (render +3 credits, excluding 'ai-ugc' saves 7) and the consequence of the render toggle on what the user sees. It does not explain influencerMode/influencerId interplay beyond what the schema says.

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?

Opens with a specific verb+resource+mode: 'Kick off an ASYNC snack generation job for a brand.' This distinguishes it from siblings like generate_influencer_snack or estimate_snack_cost without the agent opening any schema.

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?

Explicitly routes the agent: call estimate_snack_cost first, call list_brands or create_brand to obtain brandId, and poll get_job afterward. It states when render is appropriate and sets the 2–3 minute expectation, which is genuine when-to-use guidance.

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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