List published lessons in a course (course slug or ID).
| Name | Required | Description | Default |
|---|---|---|---|
| token | No | Optional fallback: your agent_signup token or paired agent key, only if your MCP client can't send an Authorization: Bearer header (the header is preferred and wins). Treated exactly like the header; never shown back. | |
| course | Yes | Course slug or ID |
Changes observed during successful MCP inspections.
Input schema / properties / tokenAdded value: +{
+ "description": "Optional fallback: your agent_signup token or paired agent key, only if your MCP client can't send an Authorization: Bearer header (the header is preferred and wins). Treated exactly like the header; never shown back.",
+ "maxLength": 200,
+ "minLength": 1,
+ "type": "string"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety and determinism are covered. The description adds one genuinely useful behavioral fact beyond them: only *published* lessons are returned, implying drafts/unpublished content are excluded. It says nothing about ordering, pagination, or result size, so it is a modest addition rather than a rich one.
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 tight sentence that front-loads the action and scope, with the clarifying parenthetical attached to the only required argument. Nothing is wasted and nothing is buried.
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 simple, fully-annotated read tool with 100% parameter coverage, the essentials are present. The notable remaining gap is the absence of any output schema and no description of what a lesson entry contains or how results are ordered, which an agent calling this cold would want.
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 100% — both `token` and `course` are fully documented in the schema, and the parenthetical '(course slug or ID)' merely restates the `course` description. Baseline 3 is correct when the schema carries the semantic load.
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 and resource ('List published lessons') with a scoping qualifier ('in a course'), which is more precise than a bare 'list lessons'. It does not, however, distinguish itself from siblings like get_lesson or get_premium_lesson, so an agent must infer the list-vs-fetch split on its own.
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 phrase 'in a course' implies the usage context (enumerate the lessons belonging to one course), but there is no explicit when-to-use, when-not-to-use, or named alternative such as get_lesson for a single lesson's detail. Usage is left to inference.
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.