Drawdown

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

Annotations already cover safety (readOnly, idempotent, non-destructive), and the description adds substantial context beyond them: NULL/NaN/infinite/non-positive values are skipped, drawdowns are fractions, and large outputs are persisted with a returned result_id. That output-storage behavior is genuinely useful and not derivable from 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?

Dense but front-loaded: the core computation comes first, then mode outputs, then edge-case handling and output persistence. No filler sentences, though the run-on structure packs many clauses per sentence.

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 return-shape burden and does so: it enumerates the max-mode columns (max_drawdown, peak_t, trough_t, recovery_t, peak_to_trough_days, n_obs) and explains series mode. An agent has what it needs to call and interpret 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 description coverage is 100%, so the schema already documents mode, result_id, value_column, and series_column. The description largely restates the mode semantics and defaults already present in the schema, adding little new parameter-level meaning. Baseline 3 is appropriate.

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 (compute drawdown) and resource (each series in a stored result of prices/equity), and defines the computation precisely (running peak, value/peak - 1). This distinguishes it cleanly from sibling analytics tools like analytics_returns and analytics_volatility without needing to name them.

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 implied usage guidance through the mode descriptions (max for summary, series for per-observation) and the note 'Use split-adjusted bars,' but never states when to prefer this over alternatives or any prerequisites/exclusions. Usage is inferable but not explicit.

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