Turn an already-generated lesson into a LinkedIn-ready share post. Pass lesson_id directly OR episode_id (uses the most recent lesson generated by the caller for that episode). Lesson must exist — chain after generate_lesson if needed.
| Name | Required | Description | Default |
|---|---|---|---|
| lesson_id | No | ||
| episode_id | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are minimal (readOnlyHint=false, destructiveHint=false); description adds context about chaining and caller-scoped lookup for episode_id, but could specify whether it posts to LinkedIn directly or just generates text.
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 concise sentences, front-loaded purpose, but could be slightly more structured (e.g., separate lines or bullet points).
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?
Covers main usage and prerequisites; missing return value description but no output schema exists; adequate for a simple 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?
With 0% schema description coverage, the description fully explains both parameters (lesson_id and episode_id), their types (uuid implied), and behavioral differences, compensating completely for the schema gap.
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 it transforms an existing lesson into a LinkedIn post, specifies two ways to identify the lesson (lesson_id or episode_id), and distinguishes from generate_lesson by noting the prerequisite of an existing 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?
Explicitly states when to use (after generate_lesson) and explains parameter alternatives, but does not provide explicit when-not-to-use or alternatives (e.g., if you need to create a lesson first, use generate_lesson).
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.