Refs

Plan a video from references

plan_video

Give a brief (product, audience, length, platform) and 1-5 references (REF ids, links or ids, or your brand films' brand:: ids), and optionally one of your imported brands (brand) to plan in its house style. Returns, per reference, the plates that carry its timing and look (format, hook, beat sheet, style, sound, edit recipe) plus its recipe prompt, and the one formula that best matches, with instructions to write a shot list and a build prompt for the user's own video. With several references it also says how to mix them: one spine for structure and timing, and one named job for each of the others. Name the spine first if you already know it. Then, like an editor, it storyboards before anything is built: you draft the storyboard (1 direction for a fixed brief, 2–4 for an open one), check it with the storyboard tool and lay it out as a Claude Design canvas, the user picks a direction, and only then do you write the build prompt. For every plate of one film, use get_blueprint.

Input Schema

TableJSON Schema

Name	Required	Description
`brand`	No	One of your brand imports (id or @handle, from list_brands): its brand kit becomes the house-style constraint
`brief`	Yes	What the user wants to make
`context`	Yes	Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
`llm_model`	Yes	The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
`reference_ids`	No	REF-0005, refs.video/v/… or YouTube/X links, ids (get_collection lists a collection's films), or brand:<import>:<film> ids. Optional with brand: then the brand's own top films are the references

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 20, 2026

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations, the description carries the full burden, and it does substantial work: it enumerates the returned artifacts per reference and describes the storyboard-before-build sequencing that governs behavior. It is thin on operational traits such as side effects (does it persist a plan or canvas?), permissions, or failure modes.

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?

Nearly all content is substantive, but it is delivered as a single run-on paragraph with heavy comma chaining, forcing the agent to parse inputs, returns, and workflow out of one block. Splitting the return description from the workflow would make it scannable without losing information.

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?

No output schema exists, and the description compensates by detailing returned plates, recipe prompts, formulas, and multi-reference mixing rules, which is enough for an agent to call it correctly. Minor gaps remain around side effects and whether the call creates any persistent artifact.

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, but the description adds real meaning: it decomposes the brief (product, audience, length, platform), expands reference id forms (REF ids, links, ids, brand:<import>:<film>), and explains that brand injects a house-style constraint and can substitute for references.

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 and resource ('plan a video from references') and immediately explains what planning yields (plates carrying timing/look, recipe prompt, best-matching formula). It explicitly segments itself from the sibling get_blueprint ('For every plate of one film, use get_blueprint'), so an agent can route correctly without reading schemas.

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?

Gives concrete use conditions (1-5 references, optionally a brand for house style; optional references when a brand is named), names the alternative tool for the adjacent case, and lays out the downstream sequence: draft storyboard, check with the storyboard tool, lay out as a Claude Design canvas, user picks a direction, then write the build prompt.

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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