get_adjust_factor_data

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the data source ('Baostock') and return format ('Markdown formatted string'), but lacks critical behavioral details such as rate limits, authentication requirements, error handling specifics, or whether the operation is read-only or has side effects. For a tool with no annotations, this leaves significant gaps in understanding its operational behavior.

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 appropriately sized and well-structured, with a clear purpose statement, usage hint, and detailed parameter documentation. It is front-loaded with the core functionality. Minor improvements could include briefer formatting, but overall it is efficient with minimal waste.

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 tool's complexity (fetching financial data with three parameters), no annotations, and an output schema present, the description is fairly complete. It covers purpose, parameters, and return format. However, it lacks some contextual details like error scenarios or data freshness, which could enhance completeness for the agent's decision-making.

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?

The schema description coverage is 0%, meaning the input schema provides no descriptive information about parameters. The description compensates fully by detailing each parameter's semantics in the Args section: 'code' specifies Baostock format with examples, and 'start_date'/'end_date' specify the required date format. This adds substantial 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's purpose with specific verb ('fetches'), resource ('adjustment factor data'), and scope ('for a given stock code and date range'). It distinguishes from siblings by mentioning the specific data source ('Baostock's "涨跌幅复权算法" factors') and use case ('useful for calculating adjusted prices'), which is unique among the listed sibling tools that focus on different financial data types.

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 implies usage context by stating the tool is 'useful for calculating adjusted prices', which suggests when to use it. However, it does not explicitly state when to use this tool versus alternatives (e.g., other data-fetching tools in the list) or provide exclusions, leaving some ambiguity for the agent in selecting among similar tools.

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