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. It mentions the data source and general use case but fails to disclose critical behavioral traits such as rate limits, authentication needs, error handling, or the structure of the returned 'Adjustment factors table.' This is a significant gap for a data-fetching tool with no annotation coverage.

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 front-loaded, with the core purpose stated first. The Args and Returns sections are structured for clarity, though the inclusion of parameter details in the description (while helpful) slightly reduces conciseness. Every sentence earns its place, but minor trimming could improve efficiency.

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 (5 parameters, no annotations, no output schema), the description is incomplete. It covers the purpose and some parameters but lacks details on behavioral aspects (e.g., rate limits, errors) and the output structure. Without an output schema, the description should explain return values more thoroughly, but it only vaguely mentions 'Adjustment factors table.'

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 0%, so the description must compensate. It provides clear semantics for 3 parameters (code, start_date, end_date) with examples and format details, which adds substantial value beyond the schema. However, it omits the 'limit' and 'format' parameters, leaving them undocumented. Since 3 out of 5 parameters are well-explained, this is above baseline but not perfect.

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') and resource ('adjustment factor data'), and distinguishes it 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'). This differentiates it from other data-fetching tools like get_historical_k_data or get_dividend_data.

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 through 'Useful for calculating adjusted prices,' which suggests when this tool might be appropriate. However, it lacks explicit guidance on when to use this tool versus alternatives (e.g., other financial data tools in the sibling list) or any prerequisites or exclusions, leaving some ambiguity for the agent.

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