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review_course

Run quality checks on a drafted course to verify all lessons exist, contain required headers, and meet minimum length. Review validation report to fix failures before publishing.

Instructions

Step 6 — Run quality checks on a drafted course before publishing.

Checks:
  - All lessons in lessons.json have a corresponding file on disk
  - Each lesson file contains the required section headers
  - No lessons are empty (< 200 chars)
  - lessons.json is valid JSON with modules and lessons arrays

Returns a pass/fail report. Fix any failures before calling publish_course().

Args:
    slug: The course slug to review.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden of behavioral disclosure. It describes the tool as performing checks and returning a report, implying it is read-only. However, it does not explicitly confirm non-destructive behavior or mention any side effects, which could be clearer.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured. It uses a brief introductory line, bullet points for checks, and a clear instruction for the result. No extraneous information is present.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a straightforward review tool with one parameter and an output schema, the description is mostly complete. It explains the checks and the expected action. However, it could mention whether the tool saves anything or provides example output, but the output schema covers the return details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description explains the parameter 'slug' with 'The course slug to review,' which adds basic meaning beyond the schema. However, with 0% schema description coverage, more detail (e.g., format or examples) would improve agent understanding, though the parameter is simple enough.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Run quality checks on a drafted course before publishing.' It lists specific checks and distinguishes itself from the sibling tool publish_course by indicating it should be used beforehand.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description positions the tool as 'Step 6' in a workflow and explicitly instructs the agent to fix failures before calling publish_course(). This provides clear usage context, though it does not mention when not to use or list alternative tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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