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Honey2339

PikoReels MCP

by Honey2339
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TypeScript
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Generate a video

generate_video

Start a text-to-video generation job from a prompt, returning a background job to poll. Costs credits per second based on model and quality.

Instructions

Start a text-to-video generation. COSTS CREDITS — priced per second, from about 5 to 53 credits a second depending on model and tier, so a 5-second clip ranges from ~25 to ~265 credits. Charged when the job is accepted, not when it succeeds. Get valid model, quality, duration and aspect values from list_studio_models, and check the balance with whoami first. Returns a job that renders in the background; poll it with get_video_job.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
audioNoWhether to generate sound. Only has an effect on models whose `audio.switchable` is true, and audio usually costs more per second.
modelYesModel id from list_studio_models (video.models[].id).
aspectNoAspect ratio from the model's `aspects`. Defaults to auto.
promptYesWhat should happen in the shot.
qualityYesQuality/tier id from that model's `qualities`. The main cost lever.
durationYesSeconds, as a string, from the model's `durations` — e.g. '5'. Multiplies cost.
useBrandNoFold the workspace's brand context into the prompt. Defaults to true; set false for a shot that has nothing to do with the brand.
workspaceIdNoWorkspace to act in. Defaults to the active one.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

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 (readOnlyHint=false, openWorldHint=true) by disclosing credit pricing per second, the charged-on-accept-not-on-success rule, and the asynchronous background-render lifecycle. These are exactly the traits an agent cannot infer from structured fields.

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?

Front-loads the cost warning, then the workflow sequence (source values, check balance, poll results). Every sentence carries actionable information with no 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?

Despite no output schema, the description explains the return (a job that renders in the background) and how to retrieve results via get_video_job. Combined with cost, prerequisites, and lifecycle coverage, nothing essential for correct invocation 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 coverage is 100% and the schema already documents each parameter, including sourcing (model from list_studio_models, quality as the main cost lever, duration from durations). The description mostly repeats the sourcing hint rather than adding new syntax or constraint detail, so the baseline 3 applies.

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 ('Start a text-to-video generation') and is clearly distinct from siblings like generate_image and get_video_job. An agent can identify this as the async video-generation entry point without opening the 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?

Explicitly names prerequisites and alternatives: pull valid values from list_studio_models, check balance with whoami first, and poll results with get_video_job. It tells the agent both what to do before and after, leaving nothing to inference.

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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