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by co.heista

Decode Video Ad

decode_ad

Decode a specific video ad URL into its full structural formula — beat-by-beat breakdown, hook classification, behavioral psychology stack, creative format, runtime performance signals (active days on Meta Ad Library when available), and per-cut visual data. Takes one video URL plus an optional idempotency_key. Returns a job_id immediately; poll with get_decode every 15s until status is "completed" (typically 45-60s end-to-end).

Use this when the user pastes an ad URL, names a specific competitor ad, asks "decode this" or "break down this ad" or "what makes this ad work", or wants sentence-level fidelity to one specific winner before writing a script with generate_adscript.

Supports Facebook Ad Library, TikTok, Instagram Reels, YouTube Shorts, and direct .mp4 URLs. Costs 15 credits for videos ≤60s, 20 credits for 61-120s.

Do NOT use to browse the corpus or find ads by category — use decoder_intelligence or adformula_intelligence (both free) for discovery. Do NOT use for image ads or static creative.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesVideo URL to decode. Supports: Facebook Ad Library, TikTok, Instagram Reels, YouTube Shorts, or direct .mp4 URL.
idempotency_keyNoOptional unique key to make this call safely retryable. If the same key + org repeats, the original result is returned without re-charging.

TDQS

A4.8/5.0
Behavior5/5

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

Discloses credit costs, polling mechanism (15s intervals, 45-60s completion), and async job pattern. Annotations do not contradict, and description adds significant behavioral context.

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?

Well-structured with core purpose first, then output format, usage, sources, cost, and exclusions. Minor redundancy but overall efficient.

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?

Despite no output schema, description explains return format (job_id, poll for results) and content of decoded result (breakdown, classification). Covers all key aspects for correct usage.

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 covers both parameters fully. Description adds value by explaining idempotency_key behavior (safe retry without re-charge) and listing supported URL types.

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?

Clearly states it decodes a video ad URL into a structural formula, distinguishing from siblings like decoder_intelligence and adformula_intelligence. Explicitly mentions supported platforms and exclusions (image ads).

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?

Provides detailed when-to-use scenarios (user pastes URL, asks for decode) and when-not-to (browsing corpus, image ads) with specific alternative tools mentioned.

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

A3.7/5.0
Disambiguation4/5

Despite the high tool count, most tools have distinct purposes with thorough descriptions that specify when to use each. Some overlap exists among creative direction tools (call_creative_worlds vs chat_with_creative_worlds), but the descriptions clarify usage patterns.

Naming Consistency3/5

Naming conventions are inconsistent overall: some follow verb_noun (create_powersource_url, decode_ad), others use noun_verb or compound names (adformula_intelligence, fleet_analytics_overview). However, subgroups like dispatch_* and list_*_presets maintain internal consistency.

Tool Count2/5

112 tools is far beyond the typical 3-15 range for well-scoped servers. While the server covers a broad domain, the sheer number likely overwhelms agents and suggests insufficient consolidation of related operations.

Completeness4/5

The tool set covers core creative intelligence workflows: brand analysis, ad decoding, script generation, creative direction, and research. Minor gaps exist (e.g., no social media publishing tools), but the main use cases are well-supported.

Resources