MightyAutomation

Generate video (HyperFrames)

grok_generate_video

Render a video through the workspace video connector (default hyperframes — AI-authored motion graphics rendered in the platform sandbox). Pass audio_job_id (from grok_generate_audio) or audio_path to mux narration under the video. Paid, metered render that runs in the background: returns immediately with render_id — call grok_render_status for progress and the result path.

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>"
    },
    "name": "grok_generate_video"
  }
}

Input Schema

Name	Required	Description
`brief`	Yes	What the video should look like — plain-English direction for the motion graphics.
`style`	No	Optional style hint, e.g. "bold kinetic type, dark background".
`connector`	No	Video provider — default "hyperframes".
`audio_path`	No	podcast-audio storage path to use as narration instead.
`audio_job_id`	No	Podcast job id (uuid) — muxes its episode audio under the video.
`transcript_id`	No	podcast_transcripts id (uuid) — recorded as the render's source for lineage.

Output Schema

Name	Required	Description
`ok`	No
`error`	No	Present only when ok is false.
`run_id`	No
`status`	No
`connector`	No
`render_id`	No

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 2, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Goes well beyond the annotations by disclosing cost ('paid, metered'), execution model ('runs in the background: returns immediately with render_id'), and the async handoff to grok_render_status. The annotations only say this is a non-idempotent, open-world write; the description supplies the operational facts an agent needs to avoid treating it as a blocking call.

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?

Three sentences, each earning its place: what it renders, how to attach audio, and the cost/async contract. The most decision-critical fact (paid, backgrounded, returns render_id) is front-loaded in the final sentence rather than buried.

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?

An output schema exists, so return values need no explanation, yet the description still names render_id and points at grok_render_status for the result path. Combined with the cost and connector-default notes, an agent has everything needed to invoke this correctly and follow up.

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 cross-tool meaning: audio_job_id is tied back to grok_generate_audio and audio_path is characterized as the narration source. It doesn't explain the transcript_id/lineage parameter or the audio_path vs audio_job_id precedence rule.

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 (render) and resource (video) with the backing connector named explicitly, plus a parenthetical clarifying what HyperFrames actually does ('AI-authored motion graphics rendered in the platform sandbox'). An agent can distinguish this from grok_generate_audio, grok_stitch, and grok_render_status without opening any 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 routes the agent forward and backward in the pipeline: audio comes 'from grok_generate_audio' and progress/results come from 'grok_render_status'. It doesn't state exclusions (e.g., when to use grok_stitch instead, or what to do if the workspace has no video connector), so it falls short of a full when/when-not treatment.

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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