get_company_monthly_revenue

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 transparently describes the return envelope (success, data, error, tool), the structure of the nested revenue_data list, and clearly states that failures are returned as error responses with possible causes such as invalid symbol, unpublished data, or source unavailability. This goes beyond minimal expectations, though it does not disclose details like data freshness or pagination.

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 with sections for summary, examples, Args, Returns, and Raises, and important information is front-loaded in the first sentence. While the first two sentences are slightly redundant, every subsequent section provides useful detail without fluff. The overall length is justified by the absence of an output schema.

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 there is no output schema and no annotations, the description fulfills the full burden by detailing the return value structure (success, data, error, tool), the nested revenue items with their fields, and failure modes. For a simple one-parameter query tool, this is complete enough for an agent to invoke the tool correctly and interpret the response without additional context.

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 input schema provides only a bare string parameter 'symbol' with 0% schema description coverage. The description compensates by defining symbol as '公司股票代碼' and giving concrete examples ('2330' for TSMC, '2454' for MediaTek). This gives an agent enough semantic understanding to format the argument correctly, though it could add format constraints such as numeric string length or exchange rules.

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 verb '取得' (get) and the resource '公司月營收資訊' (company monthly revenue information), and expands on the specific data points included (revenue, YoY growth, MoM growth). This distinguishes it from sibling tools like get_company_income_statement or get_stock_monthly_trading by focusing on monthly revenue data rather than broader financial statements or trading statistics.

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 is appropriate when querying monthly revenue data of a company, with examples for specific stock symbols. However, it does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or conditions, so the guidance remains implied rather than explicit.

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