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get_slide_content

Access lecture slide content for a module from the dataflowr/slides repo. Returns cleaned markdown for studying and review.

Instructions

Fetch the lecture slide content for a module from the dataflowr/slides GitHub repo.

Returns the slide text (Remark.js markdown, cleaned). Use this when a student wants to review lecture slides or study the theory for a module.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
module_idYes

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 full burden. It discloses the return format ('Remark.js markdown, cleaned') and the source (GitHub repo). It doesn't mention side effects, but as a read-only fetch, this is acceptable.

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 two sentences long, front-loads the core action and resource, and contains no redundant information. Every word adds value.

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 a simple single-parameter tool with output schema, the description covers the purpose, source, and return format. It could mention prerequisites (e.g., module existence) but is largely complete.

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

Parameters2/5

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

Schema coverage is 0%, so description must fully compensate. While 'module_id' is self-explanatory, the description only generically refers to 'module' without clarifying the parameter's format or role, leaving ambiguity.

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 fetches lecture slide content for a module from a specific GitHub repo. It distinguishes from siblings like get_module or get_notebook_content by specifying the resource type (slides) and source (dataflowr/slides).

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 says when to use the tool ('when a student wants to review lecture slides or study the theory for a module'). It does not mention when not to use or provide explicit alternatives, but the sibling list helps.

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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