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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety and repeatability profile is covered. The description adds useful context about the input source (closed trades) and output categories, but it does not disclose behavioral details such as computational cost, distribution shape, or failure modes when inputs are invalid.

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 single sentence is front-loaded with the primary verb and resource, and contains no filler. However, given the tool's 17-parameter complexity, the description is arguably too thin to be considered fully balanced, though economy itself is handled well.

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 17 parameters, no output schema, and only 47% schema description coverage, a one-sentence description is insufficient. It does not explain how the required parameters should be filled, what the return values look like, how protocolId relates to define_experiment_protocol, or what distinguishes this analysis from the many sibling analysis tools.

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 only 47%, and the description does not compensate for the many undocumented parameters such as symbol, timeframe, startDate, endDate, source, seed, iterations, or confidenceLevel. It only references protocolId, leaving the agent to infer the meaning and format of most required inputs.

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 uses a specific verb ('Bootstrap'), names the resource ('closed-trade P&L'), and states concrete outputs ('confidence, drawdown risk, and top-trade dependency'). It also identifies the required orchestration context ('experiment protocolId'), which clearly differentiates it from sibling analysis tools like analyze_overfit_probability or analyze_parameter_robustness.

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 provides no guidance on when to choose this tool over alternatives. It does not state when a bootstrap confidence analysis is appropriate, how it relates to define_experiment_protocol, or when sibling tools like analyze_cost_sensitivity or analyze_overfit_probability should be preferred instead.

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