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AIVideo.day

by day.aivideo
OverviewDiscussionsAdmin

Make a video

make_video

Starts a video in the user's AIVideo.day account from a written brief: script, voices, pictures or 3D characters. Spends the user's credits, at most max_credits, so get the price from quote_video and the user's agreement first. Returns video_id and render_url. The server writes the video; the MP4 file is made when render_url is opened in a browser. Read the result with get_video.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "brief": "<brief>",
      "kind": "<kind>",
      "seconds": "<seconds>",
      "max_credits": "<max_credits>"
    },
    "name": "make_video"
  }
}

Input Schema

NameRequiredDescriptionDefault
kindYes"2d" is an animated or narrated video; "3d" is a film with 3D characters.
briefYesWhat the video is about, in the user's words and language.
titleNoOptional project name.
formatNoShape: 9:16 vertical (default), 16:9 wide, 1:1 square, 4:5 portrait.
secondsYesLength in seconds. 2D: 60-3600. 3D: 10-3600.
max_creditsYesThe most this call may spend. A video that costs more is refused and nothing is charged.
request_keyNoOptional key for this request. Sent again, it returns the same video instead of making and charging for another.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations only mark this as a non-read-only, non-destructive, non-idempotent call; the description goes well beyond that by disclosing credit spend, hard capping via max_credits, the refuse-and-don't-charge failure mode, and the return payload. It also explains the unusual async behavior: the server writes the video but the MP4 only materializes when render_url is opened in a browser. Minor nuance: idempotentHint=false yet request_key yields deduplication — the description clarifies this is conditional rather than contradicting the annotation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four dense sentences with zero filler, front-loaded with the action and the cost warning before the mechanics and the follow-up. The browser-render quirk is placed after the primary description rather than burying the lede.

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?

For a credit-spending mutation with 7 parameters and no output schema, the description covers what is produced (video_id, render_url), how to retrieve the finished artifact, and how to read state afterward with get_video. Nothing an agent needs to call this safely and correctly 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%, so every parameter including the kind enum, seconds ranges, and request_key dedupe is already documented in the schema. The description adds only marginal framing around max_credits (price first, agreement first) and repeats the video_id/render_url outputs. Baseline 3 is appropriate when the schema carries the parameter burden.

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 ("Starts a video in the user's AIVideo.day account from a written brief") and immediately scopes the input domain (script, voices, pictures, 3D characters). It is clearly distinguishable from the siblings quote_video and get_video, which it names.

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 an explicit prerequisite chain: get the price from quote_video and the user's agreement first, because the call spends credits. It also names the follow-up tool ("Read the result with get_video"), so an agent knows both the gate before and the step after.

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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