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

Annotations already cover safety (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description adds genuinely useful context: the Kyle (1985) regression basis, the direction of interpretation (higher lambda = more illiquid/informed trading), and the output fields including an A-E rating.

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 purpose, then model, interpretation, outputs, and usage triggers in a logical order. Four dense sentences with no filler, though the output-field enumeration borders on what the output schema already conveys.

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?

Given an output schema exists, return values need not be re-explained; the description still summarizes them usefully and supplies the interpretive direction an agent needs to explain results. Only the lack of alternatives/prerequisites keeps it from being fully complete.

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% with a single well-documented ticker parameter (including NZX examples), so the schema carries the parameter semantics. The description adds no parameter-level detail beyond this, making baseline 3 correct.

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 metric (Kyle's Lambda liquidity score) for a specific resource (NZX company), and defines what the metric means. An agent can distinguish it from volume-related siblings like get_volume_anomalies or get_technical_signals.

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 lists trigger concepts: liquidity, trading volume, price impact, market depth, tradeable-ness. Clear positive guidance, but names no alternative tool or exclusion (e.g. when to prefer get_technical_signals or get_stock_prices).

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