Generates lesson narration audio and sentence-level timing data from a course directory for the Flow Learning System, skipping unchanged lessons and supporting per-lesson runs.
Supertonic TTS로 레슨 음성(audio/lNN.mp3)과 문장별 시각표(timing.json)를 만듭니다. 바뀌지 않은 레슨은 건너뜁니다. 레슨이 많으면 lesson_id를 하나씩 주어 나눠 실행하세요.
| Name | Required | Description | Default |
|---|---|---|---|
| force | No | ||
| voice | No | ||
| lesson_id | No | ||
| course_dir | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It usefully discloses incremental behavior (skipping unchanged lessons) and an implied runtime/long-run risk for large batches, but never states that existing mp3/timing files are overwritten, how 'force' interacts with the skip logic, or what happens on TTS failure.
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?
Three short sentences, front-loaded with what is produced, followed by skip behavior and batching advice. No filler; every sentence carries information.
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?
No output schema exists, and the description does compensate by naming the exact output artifacts. However, for a 4-parameter mutation tool with zero annotation and zero schema coverage, the undefined force/voice semantics and overwrite behavior leave meaningful gaps.
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?
Schema description coverage is 0%, so the description must compensate. It only explains lesson_id (batch one at a time) and hints at course_dir via lesson paths; 'force' and 'voice' are entirely undocumented anywhere, leaving half the parameters opaque.
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?
States a specific verb+resource: synthesizes lesson audio (audio/lNN.mp3) and per-sentence timing (timing.json) via Supertonic TTS. It is clearly distinguishable from speech_preview as a batch lesson-level operation, though it never names the sibling explicitly.
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?
Gives concrete operational guidance: unchanged lessons are skipped, and when there are many lessons, pass lesson_id one at a time and run in batches. It does not say when to prefer speech_preview or lesson_validate instead, so no explicit alternative routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.