字幕生成 + 焼付
yt-captionsWhisper-WebGPU で文字起こし、SRT/VTT 出力 or 動画焼付。3スタイルプリセット (Browser-based tool)
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
yt-captionsWhisper-WebGPU で文字起こし、SRT/VTT 出力 or 動画焼付。3スタイルプリセット (Browser-based tool)
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses browser-based operation and key outputs, but omits limitations such as WebGPU requirements, file size limits, language support, or whether the original file is modified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence front-loads the core action (Whisper-WebGPU transcription), followed by output modes and browser context. Every clause adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a browser-based tool with empty schema and no output schema, the description covers the main function, outputs, presets, and environment. It could explicitly mention the input type (e.g., video/audio file), but it is sufficient for a no-parameter web tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters, so the baseline is 4. The description adds no specific parameter details, but none are needed; the mention of SRT/VTT or burn-in and 3 style presets suggests user-selectable options.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool transcribes audio/video using Whisper-WebGPU and produces SRT/VTT files or burned-in subtitles. This specific verb+resource pairing distinguishes it from sibling yt-* tools like yt-denoise or yt-lufs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The intended use is implied: create captions from media. However, there is no explicit guidance on when to use this tool versus alternatives, when to choose SRT/VTT output versus burn-in, or any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Multiple tools have overlapping purposes, such as bg-remover, bg-remover-pro, pro-matting, and takumi all performing background removal, and upscaler/upscaler-pro being redundant. With 202 tools, an agent may easily select the wrong one despite detailed descriptions.
Most tools use a consistent kebab-case format with descriptive names like pdf-compress, image-resizer, and tax-return-calc. Exceptions like 'takumi', 'pro-matting', and '-pro' suffixes (bg-remover-pro, upscaler-pro) are minor deviations relative to the total.
202 tools is an extreme mismatch for an MCP server, far exceeding the typical 3-15 well-scoped range. The sheer volume makes it unwieldy for an agent to efficiently navigate and select the right tool.
The tool set provides extensive coverage across many domains, including PDF operations (20+ tools), image editing, financial calculations, e-commerce fee estimation, and YouTube utilities. Minor gaps exist in cross-tool integration, but the breadth is highly comprehensive for the apparent purpose.