knowledge_learn

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

With no annotations provided, the description carries full burden but offers minimal behavioral insight. '学习' (learn) suggests an educational/instructional function but doesn't clarify whether this retrieves existing knowledge, generates new explanations, provides interactive lessons, or something else. No information about output format, authentication needs, rate limits, or what constitutes successful learning.

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?

Extremely concise - a single 6-character Chinese phrase. While under-specified, every character earns its place by establishing the domain. No redundant information or unnecessary elaboration.

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?

For a 4-parameter tool with no annotations and no output schema, the description is inadequate. It doesn't explain what the tool returns (text explanations? interactive lessons? visual diagrams?), how learning is structured, or what distinguishes this from the 9 sibling tools. The agent lacks sufficient context to use this tool effectively versus alternatives.

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 description coverage is 100%, so parameters are well-documented in the schema itself. The description adds no parameter-specific information beyond implying the domains (Yijing/Bazi) which map to the 'system' enum. No additional context about how parameters interact or what constitutes valid 'topic' inputs for these systems.

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 '学习易经和八字知识' (Learn Yijing and Bazi knowledge) is a tautology that essentially restates the tool name 'knowledge_learn' in Chinese. It mentions the domains (Yijing/Bazi) but doesn't specify what the tool actually does - whether it retrieves, explains, teaches, or generates content about these topics. It doesn't distinguish from siblings like 'yijing_advise' or 'bazi_analyze' which suggest more specific actions.

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 provides no guidance on when to use this tool versus the 9 sibling tools. There's no mention of alternatives, prerequisites, or specific contexts where this learning tool is appropriate versus analysis, forecasting, or interpretation tools. The agent must guess based on tool names alone.

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