Skip to main content
Glama

image_to_video

Turn a still image into a video by creating a HappyHorse task on RunAPI. Submit the image URL and an optional prompt to receive a task ID, status, and output video URLs.

Instructions

Create a HappyHorse task on RunAPI (image to video). Returns a task id, status, and output URLs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNo
waitNoPoll until the task reaches a terminal status.
modelNoRunAPI model slug for this model line.
promptNo
timeout_msNo
callback_urlNo
duration_secondsNo
poll_interval_msNo
output_resolutionNo
first_frame_image_urlYes
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions return values but does not disclose async behavior, waiting/polling semantics (despite a 'wait' parameter and timeout options), potential side effects, or prerequisites. This leaves significant behavioral ambiguity.

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?

The description is a single concise sentence, front-loaded with the key action and result. It is efficient with words, though it sacrifices depth. This is not overly verbose, so it scores well on conciseness.

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

Completeness2/5

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

With 10 parameters, no output schema, and no annotations, this description is insufficient. It does not explain the task lifecycle, parameter usage, or how to configure the request. It provides only a minimal overview, which is inadequate for a tool of this complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 20% (2 of 10 parameters described), and the tool description adds no parameter information. It fails to compensate for the low schema coverage, leaving users without guidance on parameters like prompt, seed, duration_seconds, etc.

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?

Description clearly states the action: 'Create a HappyHorse task on RunAPI (image to video).' It specifies the verb, resource, and scope, and differentiates from sibling text_to_video by emphasizing 'image to video.' It also notes the return value (task id, status, output URLs).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The context is implied through 'image to video,' suggesting when to use it, but there is no explicit guidance on alternatives or when not to use. It does not mention relative to text_to_video or other sibling tools.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/runapi-ai/happyhorse-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server