get_lecture_content

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

With no annotations, the description fully discloses behavior: text extraction method, placeholder format for examples, aggregation of fragments for lesson nodes, empty state handling, and owner view priority. No contradictions.

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

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

The description is well-structured with bullet points, warnings highlighted, and key information front-loaded. Every sentence serves a purpose; no fluff.

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

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

Given the complexity, the description is complete: covers purpose, parameter usage, return fields, edge cases (empty content), and follow-up actions. Implicit output schema in text is sufficient.

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

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

Despite 0% schema description coverage, the tool description provides extensive semantics for all three parameters: subject_id source and aggregation, book_id default, owner fallback logic, and expected values. Adds significant value beyond the bare schema.

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

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

The description clearly states the tool retrieves lecture content for a knowledge point/lesson, explicitly positioning it as the raw material for generating questions. It distinguishes from sibling tools like save_lecture_frag by focusing on reading, and references get_question for follow-up.

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

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

Provides guidance on how to obtain subject_id via other tools, explains the behavior for different parameter values (e.g., empty book_id, owner fallback), and instructs the agent to degrade to KG + question bank when has_content is false. Lacks explicit contrast with all siblings but covers essential usage.

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