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AI Subtitle Generator

ai_subtitle_generator

Generate SRT subtitles. $0.10 per video. Runs asynchronously: returns a generation_id; call get_generation until status is "completed".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
videoYesVideo: public https URL or base64 data URI (video).
languageNoSpoken language
translateNoTranslate to English

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations provide readOnlyHint=false and openWorldHint=true, so the description's mention of asynchronous behavior and the need to poll adds valuable context beyond annotations. However, it doesn't disclose cost or rate limits, but it does mention the $0.10 per video cost, which is helpful. The async behavior is a key behavioral trait not captured in annotations.

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?

Three concise sentences with zero filler. The first sentence states the core action, the second provides pricing, and the third explains the async workflow. Effective front-loading of critical usage 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?

Given the output schema is absent, the description explicitly mentions the return of a generation_id and the need to poll get_generation, which is essential for a complete async workflow. It covers the cost and basic workflow. It doesn't describe the output format of the final SRT, but that may be acceptable since it references a sibling tool for retrieval.

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% as all three parameters have descriptions. The description does not add extra meaning beyond the schema; it mentions the language and translate parameters indirectly. Since schema covers everything, baseline 3 applies.

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?

States a specific verb and resource: 'Generate SRT subtitles' and clearly distinguishes from siblings like ai_speech_to_text and ai_audio_translator by focusing on subtitle generation. Could be more explicit about being a video-to-subtitle tool, but the purpose is clear.

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?

Implies usage by describing the async flow (returns a generation_id, poll get_generation), which tells the agent how to use it. However, it doesn't explicitly state when to choose this over alternatives like ai_speech_to_text or ai_audio_translator. The async workflow is a clear usage guideline, but exclusions are missing.

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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