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Get Topic File as Slide Images

get_topic_file

Convert lecture files from course content into page images. Fetch specific slides by page number to read diagrams, figures, and summarize lessons.

Instructions

Download a lecture file (PDF or PowerPoint) from course content and return each page/slide as an image you can read — including diagrams and figures. topicId is the id of a topic from get_content. Slide N = page N; pass pages like "4" or "2-6" to fetch specific slides instead of the whole deck. Use after get_content to answer: "Summarize lesson 2", "What is the diagram on slide 4?"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pagesNoPages/slides to render, e.g. "4", "1-5", or "2,4,7-9". Default: the whole document (capped at 30 per call; the response notes how to fetch the rest).
topicIdYesThe topic `id` from get_content, e.g. "1234567"
courseIdYesThe course org unit (ou) ID from list_courses, e.g. 123456

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNo
pagesYes
filenameYes
totalPagesYes
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 page-to-slide mapping, the ability to fetch specific pages via the pages parameter, and a 30-per-call cap with a note in the response. This is substantial behavioral context, though it doesn't mention auth requirements or error handling, so a slight deduction is warranted.

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?

Three sentences with front-loaded purpose, a precise usage example, and no filler. Every sentence contributes value. The structure is clean and easily scannable.

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 presence of an output schema, the description adequately covers purpose, usage, and behavior. It explains the relationship to get_content, page selection, and the default cap. It lacks explicit exclusions (e.g., 'not for non-lecture files'), but the examples and context make the tool's scope clear enough.

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?

Schema coverage is 100%, so baseline is 3. The description restates parameter origins (topicId from get_content, courseId from list_courses) and provides examples, but these largely mirror the existing schema descriptions. It adds minimal new semantic meaning beyond reinforcing the page-number convention.

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 downloads a lecture file (PDF/PowerPoint) from course content and returns each page/slide as an image, including diagrams. It distinguishes itself from siblings like get_content (list content) and get_drive_file (Drive files) by specifying 'from course content' and the topicId linkage.

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

Usage Guidelines5/5

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

Explicit guidance is given: 'Use after get_content' with concrete example queries like 'Summarize lesson 2' and 'What is the diagram on slide 4?'. This establishes both prerequisite and use cases, effectively guiding the agent when to select this tool over 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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