Skip to main content
Glama

Burn captions into a video

add_captions

Add subtitles to a video, permanently shown on screen. Captions are written automatically from the speech, or you can supply your own in SRT format. Style, size and position are adjustable. Cost: 3 credits per minute with automatic captions, 1 with your own SRT. Long jobs return a job_id; poll it with get_job. Result links expire in 1 hour.

Input Schema

NameRequiredDescriptionDefault
srtNoYour own captions in SRT format. Skips speech recognition
urlYesVideo: public http(s) link
colorNoText colour#ffffff
languageNoSpoken language in English, e.g. spanish. Leave empty to detect it
positionNobottom
font_sizeNo
translate_to_englishNoTranslate the speech into English

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses permanent burning (destructive to the source framing), credit cost per minute for both modes, asynchronous job behavior with job_id + get_job polling, and 1-hour result-link expiry. It omits auth/permission requirements and what happens to the original video.

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?

Five tight sentences, front-loaded with the core action, then modes, then cost, then async/expiry behavior. No filler; every sentence conveys actionable information.

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?

For a 7-param media job with no output schema and no annotations, the description covers cost, async polling and link expiry, which are the main things an agent needs. It stops short of describing the returned artifact or permission needs, but is close to complete.

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 71% and the schema already documents srt, language, color, position and font_size. The description adds only a loose mapping ('style, size and position are adjustable') that largely restates what the schema already conveys, so it adds marginal value.

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 (add/burn) and resource (subtitles/captions into a video) with the key qualifier 'permanently shown on screen'. This clearly distinguishes it from siblings like subtitle_convert (format conversion) and transcribe_media (transcript output).

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?

Explains the two operating modes (auto from speech vs. supply your own SRT) and gives the cost implication of each, plus routing to get_job for polling. However it never explicitly says when to pick this over alternatives like subtitle_convert or edit_video, so there is no when-not guidance.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.