Generate a structured AI lesson (summary, key takeaways, sections) from a completed transcription. Requires the episode to have transcription_status "completed". Consumes 1 credit, or one unit of subscription quota, on first generation for an episode; subsequent calls return the cached lesson at no cost.
| Name | Required | Description | Default |
|---|---|---|---|
| episode_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=false and destructiveHint=false. The description adds value by disclosing credit consumption on first generation and caching behavior, which goes beyond annotations and informs the agent of side effects.
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?
The description is two concise sentences, front-loading the main action and following up with prerequisites and cost behavior. Every sentence is informative 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?
The tool has low complexity (1 parameter, no output schema), yet the description covers purpose, prerequisites, cost, and caching behavior. It is fully adequate for an agent to correctly select and invoke the 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 schema has a single parameter 'episode_id' with UUID format, and schema description coverage is 0%. The description does not elaborate on the parameter beyond implying it's an episode ID. Given the simplicity, the baseline score of 3 is appropriate as the schema is sufficient but the description adds no extra semantic detail.
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 generates a structured AI lesson from a completed transcription, specifying the output components (summary, key takeaways, sections). It effectively distinguishes from sibling tools like 'get_lesson_content' which retrieves existing lessons.
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?
Explicit usage guidance: requires transcription_status 'completed' and explains credit consumption on first call with cached results on subsequent calls. This provides clear when-to-use and cost considerations.
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.
Tools are largely distinct, covering separate actions (search, get, list, generate, transcribe, save). The main potential confusion is between list_episodes and list_feed_episodes, but descriptions clearly separate local from external.
All tools follow consistent verb_noun snake_case patterns (get_, list_, search_, generate_, transcribe_, save/remove), with no mixed conventions or unexpected styles.
25 tools is at the high end for a server of this scope. The broad domain justifies many functions, but the count feels heavy and some tools could be consolidated without losing clarity.
Discovery, ingestion, transcription, lesson generation, and user account management are all well covered. Minor gaps exist, such as no way to delete or update lessons beyond save/remove.