Skip to main content
Glama

imgs2video

Combines multiple images into a static video with optional background music, voiceover, and cover image. Video length follows voiceover duration, trimming or looping music as needed.

Instructions

将一张或多张图片合成一个静态的视频,支持添加背景音乐、配音; 视频时长以配音时长为准,当配音时长小于背景音乐时长时, 会对背景音乐进行裁剪;当配音时长大于背景音乐时,会将背景音乐复制 返回的内容是一个JSON结构,其中 traceId 代表任务查询id,videoUrl 代表生成视频地址,downloadUrl代表下载链接

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bgmUrlNo背景音乐URL (可选,非必填)
voiceUrlNo配音URL (可选,非必填)
bgmVolumeNo背景音乐音量(0~100),(可选,非必填)
imageUrlsYes图片URL列表
coverImageUrlNo封面图片链接 (可选,非必填)
coverImageDurationNo 封面图片展示时长(秒)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.16

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden. It discloses that the tool returns a JSON with traceId, videoUrl, and downloadUrl, implies an asynchronous task workflow, and explains BGM trimming/looping based on voiceover duration. It does not describe behavior when voiceUrl is absent, but the core behaviors are covered.

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 compact and front-loaded with the purpose, followed by duration rules and return structure. It is slightly run-on in formatting, but each sentence provides meaningful information with no filler.

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

Completeness3/5

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

The return structure is described despite the lack of an output schema, which is good. However, it does not specify what happens when there is no voiceover or no BGM, and it does not explicitly mention polling with taskFetchByTraceID even though traceId strongly implies that. These gaps make it adequate but not fully complete.

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. The description adds value beyond the schema by explaining the relationship between voiceUrl and bgmUrl durations, including trimming and copying behavior. This cross-parameter semantic is genuinely useful to an agent.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the core action: combining one or more images into a static video with optional background music and voiceover. The verb and resource are specific, but it does not explicitly differentiate itself from sibling tools like imageGenVideo, so it loses the top point.

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 first sentence implies the use case: when you want to turn images into a video. However, there is no explicit guidance about when to choose this tool over alternatives like imageGenVideo or video2video, and no exclusion criteria are mentioned.

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

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/Runninghcm/MathMind-MCP-SERVER'

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