track_turnover_trend

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

With no annotations, the description carries full behavioral burden and does disclose genuine traits: a facts-only policy with no thresholds/judgments (v0.8.5), the period-end balance basis, and the exact output components including observed facts (monotonic trends, sign changes, numerator-denominator divergence). However, it stays silent on failure modes for invalid company names, data staleness/coverage, and any operational constraints, so it is helpful but partial.

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 structured — a front-loaded core purpose sentence followed by dedicated Args and Returns sections — and every section earns its place. Minor redundancy exists: the first paragraph already mentions CCC and fact reporting that the Returns section repeats, and the cryptic '(v0.8.5 원칙)' parenthetical could be clearer.

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 2-parameter analysis tool with an output schema present, it is largely complete: metrics, time basis, no-judgment policy, parameter formats, and return components are all covered. The remaining gaps are integration guidance (how it relates to siblings like compare_financials or track_debt_balance) and data-availability caveats, neither of which is critical for correct invocation.

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 description coverage is 0%, yet the description fully compensates: it defines company_name as either a company name or a 6-digit stock code, and documents lookback_years' valid range (1–5) and its clamping behavior (forced to 3 when out of range). This adds real semantic meaning beyond the bare schema titles and default; only minor gaps like duplicate-name resolution remain.

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 verb ('tracks') and a precise resource — five distinct turnover ratios (receivables, inventory, payables, working capital, total assets) over multiple years on a period-end balance basis — plus the companion numerator/denominator deltas and CCC. This metric list differentiates it from sibling trackers like track_debt_balance and track_capital_structure, though it never explicitly names a sibling.

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?

Usage context is implied by the detailed metric list — an agent can infer this is the tool for turnover and cash-conversion-cycle analysis — but there is no explicit when-to-use or when-not-to-use guidance, and no alternative tool is named. Among siblings like compare_financials and get_financial_summary, the selection rationale is left to inference.

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