get_lesson

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals the default compact behavior, the optional full-content flag, the inclusion of content_chars and an excerpt in compact mode, the error behavior (NOT_FOUND for non-current pairs), and the potentially long content_markdown. This is far more transparent than typical tool descriptions.

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 a single, dense paragraph that front-loads the core purpose and then efficiently covers behavior, parameters, and constraints. Every sentence adds detail: token economy, error case, and pair restriction. There is no fluff or repetition.

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 only 2 parameters, no output schema, and no annotations, the description is remarkably complete. It explains the default response shape, the optional field, the error condition, and the intended use cases. An agent can decide whether to invoke the tool and with what arguments without needing additional context.

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?

Schema coverage is 100% for both parameters, so the baseline is 3. The description adds significant value beyond the schema: it explains the default value of include_content (false), the token-economy rationale, specific use cases for true, and the constraint that lesson_id must belong to the current pair. This elevates the score above baseline.

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 opens with '读回一节课' (read back a lesson), specifying a clear verb and resource. It enumerates exactly what is returned (title, structure, publish status, body), and distinguishes itself from siblings like get_lesson_closure_state by focusing on the lesson's full content and metadata.

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?

The description explicitly states when to use the default compact mode (token economy) and when to pass include_content=true, citing concrete use cases such as grading prep and resume-teaching cold start. It also clarifies the limitation that only the current pair's lessons are accessible. It does not name alternative tools for explicit comparison, but the guidance is clear enough for an agent to decide.

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