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Vadyu AI Video Ad Studio

by com.vadyu
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vadyu_list_recipes

List all 109+ unified Composable Director Recipes (signature presets plus the Recipe Foundry catalog, each with collection, tags and theme), 22 multi-recipe 3-stage campaign_presets, all 10 custom block schemas (ai_scene, text_card, logo_outro, ken_burns_still, flash_cut_intro, cta_endcard, split_before_after, rating_social_proof, promo_urgency_slate, feature_callout_grid), synthesized audio moods (nyc_dusk, luxury_ambient, modern_pulse, none), credit rules, and optional smart recipe recommendations.

Input Schema

NameRequiredDescriptionDefault
verticalNoOptional commercial vertical filter across all 18 verticals ('horology_watch', 'skincare_beauty', 'fragrance_perfume', 'home_fragrance_candle', 'beverage_culinary', 'fine_dining_gastronomy', 'hardware_tech', 'jewelry_accessories', 'hospitality_interior', 'fitness_wellness', 'bridal_couture', 'leather_goods', 'pet_equestrian', 'yacht_aviation', 'art_collectibles', 'bespoke_tailoring', 'resort_swimwear', 'fashion_runway', or shorthand 'watch', 'skincare', 'fragrance', 'candle', 'coffee', 'gastronomy', 'hardware', 'jewelry', 'hospitality', 'athletic', 'bridal', 'leather', 'pet', 'equestrian', 'yacht', 'art', 'tailoring', 'resort', 'fashion').
max_creditsNoOptional maximum AI render credits per ad (e.g., 1 or 2) when filtering recommendations.
recommend_forNoOptional product or fashion/portrait description (e.g., 'Tall Swedish blonde supermodel on a golden runway' or 'Matte black chronograph watch'). When provided, returns the top 3 ranked recipe recommendations with pre-filled suggested_payload.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / vertical / description
      Previous value: -"Optional commercial vertical filter ('horology_watch', 'skincare_beauty', 'fragrance_perfume', 'beverage_culinary', 'fashion_runway', or shorthand 'watch', 'skincare', 'fragrance', 'coffee', 'fashion')."New value: +"Optional commercial vertical filter across all 18 verticals ('horology_watch', 'skincare_beauty', 'fragrance_perfume', 'home_fragrance_candle', 'beverage_culinary', 'fine_dining_gastronomy', 'hardware_tech', 'jewelry_accessories', 'hospitality_interior', 'fitness_wellness', 'bridal_couture', 'leather_goods', 'pet_equestrian', 'yacht_aviation', 'art_collectibles', 'bespoke_tailoring', 'resort_swimwear', 'fashion_runway', or shorthand 'watch', 'skincare', 'fragrance', 'candle', 'coffee', 'gastronomy', 'hardware', 'jewelry', 'hospitality', 'athletic', 'bridal', 'leather', 'pet', 'equestrian', 'yacht', 'art', 'tailoring', 'resort', 'fashion')."
  2. Changed1 schema field changed
    • addedInput schema / properties / vertical
      Added value: +{
+  "description": "Optional commercial vertical filter ('horology_watch', 'skincare_beauty', 'fragrance_perfume', 'beverage_culinary', 'fashion_runway', or shorthand 'watch', 'skincare', 'fragrance', 'coffee', 'fashion').",
+  "type": "string"
+}
  3. Changed2 schema fields changed
    • addedInput schema / properties / max_credits
      Added value: +{
+  "description": "Optional maximum AI render credits per ad (e.g., 1 or 2) when filtering recommendations.",
+  "type": "number"
+}
    • addedInput schema / properties / recommend_for
      Added value: +{
+  "description": "Optional product or fashion/portrait description (e.g., 'Tall Swedish blonde supermodel on a golden runway' or 'Matte black chronograph watch'). When provided, returns the top 3 ranked recipe recommendations with pre-filled suggested_payload.",
+  "type": "string"
+}
  4. First observed

TDQS

B3.2/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden. It discloses that the tool lists recipes, campaign presets, block schemas, audio moods, credit rules, and optional recommendations, but it omits return structure, pagination, or size considerations for a 109+ item catalog.

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

Conciseness3/5

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

The definition is front-loaded and confined to one sentence, but that sentence is extremely dense, listing all 10 custom block schemas and four audio moods. Those details add some value but make the description heavier than necessary for tool selection.

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

Completeness4/5

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

With no output schema and no annotations, the description does a good job enumerating the categories of data returned: recipes, presets, block schemas, audio moods, credit rules, and optional recommendations. It is not fully complete about response structure, but it covers enough for an agent to understand the catalog contents.

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 description coverage is 100%, so the three optional parameters are already richly documented. The description adds only the general notion of optional smart recipe recommendations, which is already covered by the recommend_for schema description.

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

Purpose4/5

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

The description opens with a specific verb and resource: 'List all 109+ unified Composable Director Recipes,' and enumerates what the catalog includes. It clearly identifies the resource, but it does not explicitly distinguish this tool from siblings such as vadyu_list_sequences_and_formats or vadyu_generate_video_ad.

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

Usage Guidelines2/5

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

There is no explicit guidance on when to use this tool versus alternatives. The only conditional behavior mentioned is that recommend_for returns top 3 recommendations, which is parameter-level guidance rather than tool-selection 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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