Clipia

generate_scenario

Plan a multi-scene video from a brief: an LLM director returns a structured scenario — per-scene English video prompts with durations, plus a soundtrack prompt. Feed each scene prompt to generate_video, then stitch the finished clips with compose_video. Charged in credits like chat (LLM usage).

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": {
      "prompt": "<prompt>"
    },
    "name": "generate_scenario"
  }
}

Input Schema

Name	Required	Description	Default
`model`	No	LLM model slug (omit to use the default).
`style`	No	Optional visual style, e.g. 'cinematic', 'documentary', 'product ad', 'anime'.
`prompt`	Yes	What the final video is about: product, story, mood, audience. Any language.
`scene_count`	No	Number of scenes. Omit to derive from duration (~5s per scene).
`aspect_ratio`	No		9:16
`idempotency_key`	No	Stable key for safely retrying the same scenario request without a duplicate model call or charge. Reusing it with different input returns a conflict.
`duration_seconds`	No	Target total length of the final video.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedNov 5, 2026

addedInput schema / properties / idempotency_key

Added value: +{
+  "description": "Stable key for safely retrying the same scenario request without a duplicate model call or charge. Reusing it with different input returns a conflict.",
+  "maxLength": 255,
+  "type": "string"
+}

First observedNov 2, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare the operation is not read-only, not destructive, and closed-world, so the description is not needed to establish safety. It adds real behavioral context beyond them by disclosing cost ('charged in credits like chat') and by describing the shape of the returned scenario. It stops short of explaining what a returned scenario looks like when the brief is ambiguous or how long the LLM step takes.

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?

Three sentences, each doing distinct work: purpose, workflow, cost. It is front-loaded with the core definition and never repeats the tool name or annotations. The clause listing output elements is slightly dense but is the justification for the score rather than waste.

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?

With seven parameters, no output schema, and an LLM-backed synchronous generation step, the description covers what an agent needs: inputs come from the brief, the return is a structured scenario with per-scene prompts and a soundtrack prompt, and the next steps are named. Because no output schema exists, this inline description substitutes adequately.

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 86%, so nearly every parameter is already documented in the schema, including idempotency semantics and scene_count defaults. The description mentions durations and scene prompts only in passing and adds no format guidance beyond what the schema provides, making the baseline of 3 appropriate.

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 multi-scene video from a brief') and precisely describes the artifact produced: per-scene English video prompts with durations plus a soundtrack prompt. It names the sibling tools generate_video and compose_video, so an agent can place it in the pipeline without inspecting other 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?

Explicitly prescribes the downstream workflow: feed each scene prompt to generate_video, then stitch with compose_video. This distinguishes it from calling generate_video directly and tells the agent exactly what to do after the call returns.

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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