get_price_on_date

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. While it states the tool retrieves price data, it doesn't describe what the return value looks like (e.g., open/close/high/low prices), error conditions (e.g., invalid date or symbol), rate limits, or authentication needs. For a read operation with zero annotation coverage, this is a significant gap in transparency.

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 concise with two sentences: a purpose statement followed by parameter documentation. It's front-loaded with the core functionality. However, the Japanese-to-English mix might cause minor parsing issues, and the structure could be slightly improved by integrating the parameter details more seamlessly.

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 (2 required parameters, no output schema, no annotations), the description is minimally adequate. It covers the purpose and parameters well but lacks output format details, error handling, and differentiation from siblings. For a data retrieval tool, the absence of output information is a notable gap, though the parameter documentation helps compensate partially.

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 explicitly documents both parameters in the 'Args' section: 'symbol: 銘柄コード' (stock code) and 'date: 日付 (YYYY-MM-DD)' (date in YYYY-MM-DD format). With 0% schema description coverage, this fully compensates by providing clear semantic meaning beyond the bare schema. The date format specification is particularly valuable.

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: '特定日の価格データを取得' (Get price data for a specific date). It specifies the verb ('取得' - get) and resource ('価格データ' - price data) with temporal scope ('特定日' - specific date). However, it doesn't explicitly differentiate from siblings like get_price_range or get_yearly_summary, which also retrieve price data but with different temporal scopes.

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 sibling tools like get_price_range (for date ranges) or get_yearly_summary (for annual data), nor does it specify prerequisites or exclusions. The agent must infer usage from the tool name and description alone.

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