get_candlestick_data

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate read-only and idempotent behavior; the description adds valuable context such as 'cache-only, no API call', the return format (parallel arrays for Plotly/Recharts), detailed return keys including lock_days and earnings_dates, and the error response format. This goes well beyond the structured annotations.

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?

Although the description is lengthy, it is well-structured with clear sections (Args, Returns) and every sentence adds value. The parameter details and return schema are necessary given the tool's complexity, and the formatting enhances scannability.

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 no output schema and 5 parameters, the description provides a complete picture: all parameters, return structure, error handling, plan coverage, and cache behavior. There is no missing critical information for correct invocation and interpretation of results.

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 has 0% coverage, so the description fully compensates by explaining every parameter: code with examples, from_date/to_date formats and defaults, indicators default and options list, and adjusted default. All 5 parameters receive clear semantic meaning.

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?

Description clearly states 'Return candlestick OHLCV + indicator data as JSON' with a specific verb and resource, and further specifies use cases (ローソク足・株価チャート・React artifact queries). It distinguishes itself from sibling tools by explicitly directing multi-stock comparison users to get_comparison_chart_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?

Explicit usage guidance is provided: 'Use for ローソク足・株価チャート・React artifact チャート queries (JSON format)' and 'For multi-stock comparison use sibling get_comparison_chart_data.' It also states plan support and cache-only behavior, helping the agent decide when to invoke this tool.

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