get_required_reserve_ratio_data

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 states the tool fetches data and returns a markdown table or error, but lacks details on permissions, rate limits, data freshness, or error conditions. For a data-fetching tool with zero annotation coverage, this is insufficient to inform the agent about operational constraints or risks.

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 well-structured and appropriately sized, with a clear purpose statement followed by Args and Returns sections. Every sentence adds value, such as clarifying date formats and year_type options. It could be slightly more concise by integrating the purpose with parameter details, but overall it's efficient and front-loaded.

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 moderate complexity (3 parameters, no annotations, but with an output schema), the description is partially complete. It covers parameters well and mentions the return format (markdown table or error), but lacks behavioral context like data sources, update frequency, or error handling. The output schema likely details the return structure, so the description doesn't need to explain return values, but it should address other operational aspects.

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 description adds significant meaning beyond the input schema, which has 0% description coverage. It explains all three parameters: start_date and end_date as optional date ranges in 'YYYY-MM-DD' format, and year_type as optional with enums '0' for announcement date (default) and '1' for effective date. This fully compensates for the schema's lack of descriptions, providing clear semantics and usage examples.

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 'fetches required reserve ratio data (存款准备金率) within a date range,' specifying both the verb ('fetches') and the resource ('required reserve ratio data'). It distinguishes itself from siblings by focusing on reserve ratio data rather than stock, financial statement, or rate data. However, it doesn't explicitly contrast with similar tools like get_deposit_rate_data or get_money_supply_data, keeping it from a perfect score.

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 alternatives. It doesn't mention any prerequisites, such as needing specific permissions or data availability, nor does it suggest other tools for related tasks like get_deposit_rate_data or get_money_supply_data. The lack of usage context leaves the agent without direction on tool selection.

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