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save_course_sources

Save harvested source metadata for a course by passing a JSON array of source objects with title and at least one identifier (url, file_path, or doi) to persist as YAML files.

Instructions

Step 3 — Save harvested source metadata for a course.

Call this after the Content Harvester has collected materials. Pass a
JSON array of source objects. Each source is written as a YAML file in
`knowledge/courses/{slug}/sources/` and the build advances to Step 4.

Args:
    slug: The course slug.
    sources: JSON array of source dicts — each must have at least a
             ``title`` and one of ``url``, ``file_path``, or ``doi``.
             Example: '[{"title": "OpenIntro Stats", "url": "https://openintro.org/book/os/", "type": "textbook"}]'

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYes
sourcesYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses that each source is written as a YAML file in a specific directory and that the build advances to Step 4. It does not cover authorization, rate limits, or idempotency, but the core behavior is clear.

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, with the purpose stated upfront. It uses a structured format (Args) to detail parameters. Every sentence adds necessary information without redundancy.

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?

Given the tool's simplicity (2 required parameters, no enums, output schema exists), the description adequately covers behavior and parameter semantics. It explains the output effect (files written, build advancement). It does not discuss edge cases or error handling, but relevant information is present.

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?

The schema properties have no descriptions (0% coverage), but the description fully explains both parameters: 'slug' as the course slug, and 'sources' as a JSON array with required fields (title and one of url/file_path/doi). It provides a concrete example, greatly adding value.

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 identifies the action (save), the resource (harvested source metadata), and the context (for a course, Step 3 in a build pipeline). It distinguishes from sibling tools by specifying its position in a workflow and the effect of advancing to Step 4.

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 explicitly states when to use this tool ('after the Content Harvester has collected materials') and mentions it advances the build to Step 4, providing workflow context. However, it does not explicitly state when not to use it or offer alternatives.

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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