get_operation_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 return format ('Markdown table with operation capability data or an error message'), which is helpful, but does not cover other traits like rate limits, authentication needs, error conditions beyond generic messages, or data freshness. For a data-fetching tool with zero annotation coverage, this leaves significant gaps.

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: a clear purpose statement followed by 'Args' and 'Returns' sections with bullet points. Each sentence earns its place by providing essential information without redundancy, making it easy to scan and understand.

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 3 parameters), no annotations, and an output schema (implied by 'Returns' statement), the description is largely complete. It covers purpose, parameters, and return format. However, it lacks behavioral details like error handling specifics or data source context, which could enhance completeness for a tool with no annotations.

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 substantial meaning beyond the input schema, which has 0% description coverage. It explicitly documents all three parameters ('code', 'year', 'quarter') with examples and constraints (e.g., '4-digit year', quarter as 1-4), fully compensating for the schema's lack of details. This provides clear semantic context for parameter usage.

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: 'Fetches quarterly operation capability data (e.g., turnover ratios) for a stock.' It specifies the verb ('fetches'), resource ('quarterly operation capability data'), and scope ('for a stock'), but does not explicitly differentiate it from sibling tools like 'get_hk_operation_data' or 'get_balance_data' which might fetch similar financial data for different markets or aspects.

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. With many sibling tools fetching financial data (e.g., 'get_balance_data', 'get_profit_data', 'get_hk_operation_data'), it lacks explicit context, prerequisites, or exclusions to help an agent choose appropriately. Usage is implied by the data type but not clearly distinguished.

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