ai-content-drop

Write ad storyboard

write_ad_storyboard

Read-onlyIdempotent

Turn an ad brief into a storyboard: 1-5 shots with framing, the spoken line, the caption and the duration of each. Costs no credits. product_image_url, when the product image is already known, is passed as context for the storyboard. The storyboard, as returned or edited, is what estimate_ad_cost prices and generate_ad renders. SIGN-IN REQUIRED: connect this server with OAuth (the host prompts for it), or add an API key header "Authorization: Bearer acd_live_…" created at https://aicontentdrop.com/settings/integrations.

Input Schema

Name	Required	Description	Default
`brief`	Yes	The ad brief: brandName, brandOneLiner, valueProps (1-8 strings), cta, intent, format (an id from list_ad_formats), shotCount (1-5), totalDurationSec (5-20), aspect (9:16 \| 16:9 \| 1:1), language, quality (fast \| premium), modelTier (standard \| cinematic), garmentImageUrl for the try-on formats.
`product_image_url`	No	Optional public https URL of the product image, the same one generate_ad will take, passed as context for the storyboard.

Output Schema

Name	Required	Description
`format`	No
`next_step`	Yes
`storyboard`	Yes	ad { aspect, totalDurationSec, voiceLanguage } and shots[].

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 26, 2026

addedInput schema / properties / product_image_url

Added value: +{
+  "description": "Optional public https URL of the product image, the same one generate_ad will take, passed as context for the storyboard.",
+  "type": "string"
+}

Changed1 schema field changedSep 30, 2026

changedInput schema / properties / brief / description

Previous value: -"The ad brief: brandName, brandOneLiner, valueProps (1-8 strings), cta, intent, format (an id from list_ad_formats), shotCount (2-5), totalDurationSec (6-20), aspect (9:16 | 16:9 | 1:1), language, quality (fast | premium), modelTier (standard | cinematic), garmentImageUrl for the try-on formats."New value: +"The ad brief: brandName, brandOneLiner, valueProps (1-8 strings), cta, intent, format (an id from list_ad_formats), shotCount (1-5), totalDurationSec (5-20), aspect (9:16 | 16:9 | 1:1), language, quality (fast | premium), modelTier (standard | cinematic), garmentImageUrl for the try-on formats."

AddedSep 11, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Adds real context beyond the annotations: 'Costs no credits' reinforces the readOnly/non-destructive hints, and the SIGN-IN REQUIRED block discloses the exact OAuth and 'Authorization: Bearer acd_live_…' requirements plus where to create the key, which the annotations do not carry. Missing only edge behavior such as whether repeated calls produce identical storyboards despite idempotentHint.

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-loads the core action and output shape in the first sentence, with pipeline and auth details following. The auth block is long but each element (OAuth vs API key, header format, settings URL) is actionable; slight density keeps it from a 5.

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?

Given a nested-object schema with full descriptions, an output schema, and read-only annotations, the description covers the remaining agent needs: cost, auth, and downstream consumption by estimate_ad_cost and generate_ad. Nothing critical is missing for correct invocation.

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, and the description earns more: it explains that product_image_url is the same image generate_ad will take and is passed as storyboard context. The brief's fields are left to the schema, which already documents them thoroughly.

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 concrete verb and resource ('Turn an ad brief into a storyboard') and specifies the output shape (1-5 shots with framing, spoken line, caption, duration). It also positions itself precisely against estimate_ad_cost and generate_ad, so an agent can place it in the pipeline without opening the schema.

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 says the returned/edited storyboard is what estimate_ad_cost prices and generate_ad renders, which is a clear ordering rule for the sibling tools. It does not give explicit exclusions (e.g. when to skip the storyboard and call generate_ad directly), so it stops short of the top score.

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

All ai-content-drop tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.