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

Beyond the readOnly and idempotent annotations, the description discloses the tool's contents (hard rules for bias, outliers, sampling, day-of-week, risk metrics), the required call order, and that it should be invoked before writing SQL. This adds meaningful behavioral context without contradicting any annotation.

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 dense but well-organized, starting with a succinct summary, followed by a required-order callout, then a comprehensive use-case list. Every sentence provides actionable value, though the use-case enumeration is slightly long and could be condensed.

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 the zero-parameter schema, an output schema, and informative annotations, the description fully covers the necessary context: when to invoke, prerequisites, contents, and combinability. No critical information is missing for selecting and using this tool.

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?

The tool has zero parameters, and the schema coverage is 100%, so the baseline is 4. The description appropriately adds no parameter-specific details since none exist, and it doesn't need to compensate for any schema gaps.

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 clearly states the tool's function as 'Backtesting and simulation guardrails' and lists specific metrics and biases it covers (survivorship, drawdown, Sharpe, day-of-week). This distinguishes it from sibling workflow loaders by domain and makes the purpose unmistakable.

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?

The description gives explicit trigger conditions ('when the user asks to backtest, simulate, validate...') and mandatory prerequisite ordering ('REQUIRES get_database_schema then get_query_patterns to be called first'). It also notes that the tool can be combined with other workflows, providing comprehensive usage guidance.

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