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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, non-destructive, closed-world), and the description adds genuinely useful behavior beyond them: the series is stitched together from several periodic reports, and missing quarterly figures are computed from cumulative values, which tells the agent the data is derived rather than reported. It still says nothing about output shape or how gaps/partial history are represented.

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?

Front-loaded with the core operation and the derived-data caveat, then the long topic list, which is justified because it supplies the missing topic parameter documentation. The dense bullet-style enumeration is efficient, though the single-sentence run-on makes it slightly harder to scan.

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?

For a 6-parameter read-only analytical tool with no output schema and strong annotation coverage, the description covers what the tool produces, the topic space, and the discovery fallback. What remains (exact return shape) is acceptable to omit given there is no output schema to defer to and the tool is read-only and idempotent.

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 83%, so the baseline is 3, but the description goes further by supplying the semantics of the otherwise undocumented 'topic' parameter through a full value taxonomy and by explaining the empty-topic mode switch. Example values for dim (DS, HBM, 미주) and freq behavior add a bit more, though the description does not clarify how dim/item interact with topic.

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?

States a specific verb and resource: a quarterly/annual time series built by concatenating multiple periodic reports, with quarterly values derived from cumulative figures when absent. The enumerated topic taxonomy (segment, product_sales, region_sales, capacity/production/utilization, capex, rnd, market_share, inventory, cost_by_nature, sga, raw_materials, material_prices, backlog, channel) makes it unmistakable versus siblings like financials or earnings.

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?

Gives a concrete discovery path: leaving topic empty returns the list of topics available for that company, which tells the agent how to explore before querying. It does not explicitly state when to prefer financials or earnings over this tool, so the routing guidance is strong but not complete.

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