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

Annotations already declare this is a non-destructive, read-only, closed-world read, so the safety profile is covered. The description adds substantive context beyond them: it enumerates the exact metadata returned (id, display name, category, parameters with defaults and bounds, short description) and preempts a likely agent misconception by stating no market data or fitted values come back.

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 and return contract in the first two sentences, then a scoping exclusion, then a compact Args block. No redundancy with the name or with the annotations.

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?

No output schema exists, and the description compensates by specifying exactly which fields come back and what does not. Both parameters are documented, and with annotations covering the read-only safety profile there is nothing an agent needs to call this correctly that is missing.

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%, so the description must carry the parameter burden, and it mostly does: it explains that category is an optional filter with concrete examples ('mean_reversion', 'trend', 'pairs', 'regime') and that search is an optional case-insensitive substring matched against id and name. It does not say whether the two filters combine with AND or how partial matches rank, leaving a small gap.

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?

Opens with a specific verb+resource ('List the stochastic methods QuanterLab supports') and ties it to the consuming sibling, start_stochastic_backtest, via the exact field it feeds (primary_method.id). An agent can distinguish it from list_indicators, list_primitives, or list_fundamental_analyzers without opening a schema.

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 states the use case: 'Use this to discover valid primary_method.id values for `start_stochastic_backtest`', which gives a clear trigger condition. It also carves out a negative boundary ('Does NOT return any market data or fitted parameter values'), though it names no alternative discovery tool for that sub-need.

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