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AI Avatar Presenter & Explainer Video Maker - ManyMotions

Prepare Presenter Video

prepare_presenter_video

Prepare a talking AI avatar presenter video with ManyMotions: an explainer or lesson that teaches one concept, or a self-introduction for a person, a business or a channel, spoken to camera by an AI presenter with an AI voice. Not an advertisement: no product pitch and no call to action. ManyMotions writes the script scene by scene, generates the keyframes and the scene videos with AI voice, and edits the final cut. Returns the plan: scene count and credits required. Nothing is generated and no credits are spent. No sign-in is needed to prepare. New videos from text only: do not use it for uploaded or existing footage, photos of real people, editing, captions or dubbing; for those, point the user to ManyMotions.

Input Schema

NameRequiredDescriptionDefault
topicYesWhat the video explains, or who or what it introduces.
formatNoAspect ratio: 9:16 (default, TikTok, Reels, Shorts), 1:1, 4:5 or 16:9 (YouTube).
audienceNoWho it is for and their level, e.g. high-school students, new employees, beginners.
durationNoTarget length: 8s = 2 scenes, 15s = 3 (default), 30s = 5, 45s = 7, 60s = 8. 250 credits per scene.
languageNoSpoken language. Default English.
presenterNoHow the AI presenter looks and sounds, e.g. a friendly teacher in her 40s in a bright classroom.
key_pointsNoPoints, facts or steps that must be covered.
video_typeNoexplainer (default): teaches one concept, lesson or how-to; introduction: introduces a person, a business or a channel.
speech_speedNoDefault medium.

Output Schema

NameRequiredDescriptionDefault
runNo
stateYes
scenesNo
balanceNo
messageYes
projectYes
dashboard_urlYes
credits_requiredNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Adds behavior beyond the annotations: it returns a plan (scene count and credits required), spends no credits, and requires no sign-in. With readOnlyHint=false the annotation alone would not tell an agent that nothing is generated, so this is valuable disclosure. It stops short of describing plan validity or what happens on repeat calls (idempotentHint=false), leaving a small gap.

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 purpose, then scope exclusions, then the return/cost contract. Dense but each sentence carries information; the long semicolon-chained exclusion sentence is slightly harder to parse than it needs to be.

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?

Given an output schema exists, return values need not be detailed, yet the description still surfaces the key contract (plan returned, no credits spent, no sign-in). For a 9-parameter tool with 100% schema coverage, nothing an agent needs before calling is missing.

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% and the schema already documents the duration-to-scene mapping and the 250-credits-per-scene cost, so the description adds little parameter meaning beyond restating credit/scene output. Baseline 3 is appropriate when the schema carries the weight.

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 ('Prepare a talking AI avatar presenter video with ManyMotions') and immediately names the two supported genres, explainer/lesson vs self-introduction. It also draws an explicit boundary ('Not an advertisement') that separates it from the sibling generate_video, so an agent can disambiguate without opening either schema.

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 explicit when-not conditions: not for uploaded or existing footage, photos of real people, editing, captions or dubbing, and routes those cases to ManyMotions. It also clarifies this is text-only and preparation-only, which prevents the most likely misinvocation.

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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