Fetch the full body of one AI-generated lesson (title, summary, key takeaways, structured sections). Use to retrieve prior synthesis — for creating a NEW one call generate_lesson.
| Name | Required | Description | Default |
|---|---|---|---|
| lesson_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly and non-destructive. Description adds detail on content (title, summary, etc.) and confirms AI-generated origin. No contradictions.
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?
Two efficient sentences with zero redundancy. Purpose and usage guideline front-loaded.
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?
Low complexity tool with one param and no output schema. Description covers return contents adequately. Could mention output format, but not essential.
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?
Single parameter lesson_id with schema (uuid) but no description coverage. Tool description adds no additional meaning or source guidance. Minimal value beyond schema.
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?
Clear verb+resource ('Fetch the full body') with specific content list. Explicitly distinguishes from sibling generate_lesson.
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?
States when to use (retrieve prior synthesis) and when not (creating new, call generate_lesson). Provides clear alternative.
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.