list_lecture_docs

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

No annotations provided, but description fully discloses the GET request, return structure, empty state behavior, and evolution of backend structure. Lacks details on authentication or side effects, but as a read list, it's sufficient.

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?

Description includes implementation details (port, endpoint) that are not essential for an agent, but is still relatively short. Could be more streamlined.

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?

Covers purpose, parameter, return structure with example fields, and edge case (empty catalog). Missing error handling details, but output schema likely fills that gap.

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?

Input schema has 0% coverage, but description explains the single parameter book_id with default behavior (empty = server default), adding value beyond the 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 lists the lecture catalog, specifying which class hours/knowledge points have fragments, and distinguishes from siblings like get_lecture_content by focusing on the index/catalog.

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?

Usage is implied (to find lectures with fragments for question generation), but no explicit when-to-use or when-not-to-use as compared to other lecture tools.

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