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create_character

Create a reusable character for the user's videos and save it to their Digital Maker account, from a written description. (A character is drawn INTO the scene pictures. For a talking on-screen host, an AI presenter or avatar, use draw_presenter instead.) Every character is drawn the same way: a FULL BODY figure, head to feet, standing on a plain white background, so it can be dropped into any scene. Once saved, pass its name to create_video and that same character appears in every scene. It also lands in the user's image library. Users keep up to 10.

IF THE USER SHARES A REFERENCE IMAGE (a screenshot of a channel they like, a style they want): look at it yourself and write your own description of the KIND of character it is, then pass that. Describe the archetype, art style, colour palette, build, hair, clothing and general vibe. Ignore the framing and background of what they shared, since the result is always a full-body figure on white regardless. Do NOT try to reproduce a specific individual's face, and do not describe a real identifiable person. The point is a character of a similar style that belongs to this user, not a copy of someone else's.

Costs one image from the user's monthly allowance. Show them the result and offer to redo it before they build a video around it. Available on any plan that includes images, which is Pro and above, even though faceless video itself needs Premium: a Pro user can build their character library now and use it after upgrading.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesShort name the user will refer to the character by, e.g. Maya. Must be unique among their characters.
styleNoThe visual world this character belongs to. Use the SAME value you will later pass as style_preset on create_video, so the character matches its own video. When the user shares a reference image, pick the preset whose look matches it: flat 2D illustration is animated_cartoon; dark moody photoreal lit by a single rim light is motivational_dark; gold, marble and wealth imagery is finance_luxury; candlelit period detail is history_ancient; cosmic and nebula lighting is space_scifi; soft neutral tones with lots of negative space is minimalist_clean; ordinary present-day photography is cinematic_documentary. Defaults to cinematic_documentary.
descriptionYesFull visual description: age range, build, hair, clothing, colour palette, art style, expression, overall vibe. The richer this is, the more consistent the character stays across scenes. Avoid naming real people.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full burden and does so: it discloses the fixed rendering contract (full-body, white background, identical in every scene), the cost (one image from the monthly allowance), the account limit (10 characters), the plan gate (Pro and above even though faceless video needs Premium), and the side effect of landing in the image library. It also sets the expectation to show the result and offer a redo.

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-loaded with the purpose and the sibling disambiguation before the procedural detail, and every paragraph covers a distinct concern (rendering contract, reference-image workflow, cost/plan). It is long and slightly repetitive around the reference-image handling, but no sentence is filler.

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?

There is no output schema and no annotations, so the description must supply everything an agent needs before calling: what gets created, how it looks, what it costs, plan eligibility, limits, and what to do with the returned character afterwards. All of that is present.

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% and the schema already documents all three parameters, including the style enum mapping, so the baseline is 3. The description adds real value beyond the schema by specifying what to put into the description parameter when a reference image is shared (archetype, art style, palette, build, hair, clothing, vibe) and by reinforcing the uniqueness requirement on name.

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 ('Create a reusable character ... save it to their Digital Maker account, from a written description') and immediately contrasts with the sibling draw_presenter for talking hosts. The 'drawn INTO the scene pictures' clause disambiguates it from presenter/avatar tools an agent might otherwise confuse it with.

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?

Explicit when-to-use (a character drawn into scenes), explicit when-not (talking on-screen host -> draw_presenter), plus the downstream workflow (pass the name to create_video). It also gives a concrete procedure for the reference-image case, including what to ignore (framing, background) and what not to do (reproduce a real person's face).

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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