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Metis · Course Builder — Save Lesson Draft

save_lesson_draft

Save a drafted lesson to disk as markdown after validating required sections. Returns confirmation of written file or rejection listing missing sections.

Instructions

Step 5 of the course build — save one drafted lesson to disk.

Writes a single lesson's markdown into the course's lessons folder; call it
once per lesson during drafting. The content is validated and rejected
unless it contains all required sections, in order:
## Learning objectives, ## Prerequisites, ## Content (with ### Section N:
subsections), ## Summary, ## Exercises, ## Further reading. The filename is
derived from the lesson number and its title in lessons.json.

Args:
    slug: The course slug; selects the
        knowledge/courses/<slug>/lessons/ folder to write into.
    lesson_id: The lesson id, which must match an id in lessons.json
        (e.g. "lesson-01"); used to look up the title and build the filename.
    content: The full markdown body of the lesson, including all required
        sections listed above.

Returns:
    A confirmation with the written file path, or a rejection message
    listing the required sections that are missing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYes
contentYes
lesson_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations provided, but description details validation requirements (required sections in order), filename derivation, and return types (confirmation or rejection). Does not mention overwrite behavior or permissions, but adequately covers key behaviors.

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?

Description is front-loaded with purpose and context, then validation rules, then parameters. Every sentence adds value; no wasted words.

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

Completeness5/5

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

Given the tool's simple function (save a lesson draft), the description covers purpose, usage, validation, and return values. An output schema exists, so return explanation is sufficient.

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

Parameters5/5

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

Schema coverage is 0% (no descriptions in input schema), but the description includes a full Args section explaining each parameter's purpose and usage, fully compensating for the gap.

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?

Description clearly states 'Step 5 of the course build — save one drafted lesson to disk', specifying verb (save), resource (drafted lesson), and context, distinguishing it from sibling tools like save_course_curriculum or publish_course.

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?

Explicitly says 'call it once per lesson during drafting', providing clear context and expected frequency. Does not explicitly exclude alternatives, but sibling tools are distinct operations.

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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