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

Annotations only declare readOnlyHint=true, and the description goes well beyond that: universe exclusions (preferred/class shares, TOKYO PRO Market stocks; market=tokyoPro narrows to that segment only), no market-cap floor, and counting rules (no-trade stocks and stocks whose 52-week high equals the low are not counted as new highs/lows). The only missing behavioral context is anything about output shape or ordering, which is partly covered by the "one row per sector" statement.

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 metric list is front-loaded and the counting/exclusion caveats follow logically, so the structure serves scanning. It is dense and the trailing Japanese restatement largely duplicates the English enumeration, which costs some economy, but no sentence is filler in substance.

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?

With no output schema, the description carries the burden of saying what comes back, and it enumerates every returned metric plus the row granularity for the sector variant. Combined with the eligibility rules and token cost, an agent has enough to call it correctly and interpret the result.

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 coverage is 100%, so the baseline is 3, but the description adds genuine meaning on top: stocks with no sector code appear only in the whole-market row (an edge case the schema does not state), and it clarifies the market-omitted vs tokyoPro distinction and the common-stocks-only default behind includeEtf. This is meaning beyond the structured fields rather than repetition.

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 names a specific resource (advancers, decliners, unchanged, advance/decline ratio, new 52-week highs/lows, trading value for the latest trading day) with a sector-level variant, which is far more specific than the bare tool name. It does not explicitly distinguish itself from the closest siblings that overlap on this data, notably get_new_highs_lows, get_market_movers and get_sector_performance, so an agent must still infer which to pick.

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?

It clarifies the two modes of use — whole-market row first, optionally one row per TSE 33-sector via groupBy=sector — and notes the 1-token cost, which helps an agent decide to call it. However, there is no explicit "use this instead of X" guidance against the overlapping siblings, so selection remains implied rather than stated.

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