get_comparison_chart_data

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context beyond annotations: cache-only behavior ('cache-only, no API call'), plan support, and the exact return format including error shape. This enriches the agent's understanding of performance and side-effect profile.

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-organized with clear sections for purpose, use cases, plan support, arguments, and return values. Every sentence adds meaningful information—no filler. The structure is front-loaded with the core purpose and use cases, then detailed parameters.

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?

Despite having no output schema, the description fully specifies the return dictionary keys, the shape of records (Recharts-ready), series_keys, and error format. It covers all necessary context: when to use, parameter details, data shape, and behavioral notes (cache-only). Nothing critical is missing.

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 has 0% description coverage, so complete burden falls on the description. It fully explains each parameter: codes (1-10, with examples), date formats and inclusivity, mode options with default and meaning, and labels as optional custom legend. This exceeds what the schema alone provides.

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 uses a specific verb ('Return time-series data') with a clear resource ('multi-stock comparison') and scope ('up to 10 codes'), instantly distinguishing it from the sibling candlestick tool. The Japanese labels reinforce the exact purpose, leaving no ambiguity about what this tool does.

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?

Explicitly states when to use: 'Use for 比較チャート・パフォーマンス比較・リターン比較・relative performance queries', and provides an explicit alternative: 'For ローソク足・candlestick charts use sibling get_candlestick_data (returns JSON)'. This is a model example of usage guidance.

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