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Generate a video

generate_video

Create videos from text prompts or animate a still image with optional end frames in Tanvo MCP; returns an ID for polling or waits for the finished clip.

Instructions

Text to video, or animate a still when image is given (optionally ending on end_image). Video takes one to several minutes, so by default this returns at once with an id for get_generation. Needs TANVO_API_KEY.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
waitNoWait for the clip (up to 8 minutes)
audioNoNative sound on models that offer it
imageNoStart frame: a local path or a public https URL
modelNoModel id from list_modelsstudio-video-v1
aspectNo16:9, 9:16, 1:1 … (default: the model's)
promptYesOne scene: subject, action, camera movement, light and style
save_toNoOptional folder to download the results into (defaults to TANVO_OUTPUT_DIR when set)
durationNoSeconds; one of the model's options (default: the model's)
end_imageNoEnd frame on models that support it
resolutionNo480p, 720p, 1080p or 4K where supported (default: the model's)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.2/5.0
Behavior4/5

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

With only openWorldHint annotated, the description carries the burden and delivers useful traits: generation takes one to several minutes, the call returns immediately with an id by default, and it needs TANVO_API_KEY. It stops short of disclosing cost or the exceptions to the default async return.

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?

Two tightly packed sentences with zero filler; the capability, the latency/default behavior, and the auth requirement are all front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, yet the description covers the essential return contract (id for get_generation) and auth. It omits how save_to affects returns, but the schema covers that parameter fully.

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 the baseline is 3. The description adds only the image/end_image pairing semantics (start frame vs ending frame), which the schema largely already conveys per-parameter.

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?

The description names a specific verb+resource ('Text to video') and clarifies the alternate mode ('animate a still when `image` is given'), which cleanly differentiates it from the sibling generate_image tool without requiring a schema read.

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?

It explains the default behavior and routes the agent onward ('by default this returns at once with an id for get_generation'), which is clear usage context. It does not explicitly contrast when to choose this over generate_image or generate_from_app, so it falls short of full when/when-not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.