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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds genuinely useful traits beyond that: it can return 'inconclusive/blocked' results and it 'streams native events', which an agent could not learn from the annotations. It still omits what conditions cause a blocked outcome and what streaming implies for the caller.

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?

It is short and reasonably front-loaded, but the telegraphic, jargon-heavy style ('simultaneous 95% sampling bounds', 'Independent-uniform durations') costs clarity, and the trailing 'Streams native events' fragment feels appended rather than structured.

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?

For a 9-parameter computational tool with no output schema and no schema descriptions, the description is too thin: it does not explain the required inputs (items, budget, uncertainty), the meaning of the sampling/model parameters, or the shape of the streaming result. Annotations cover safety but not the input/output contract.

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% across 9 parameters, so the description carries the full burden and largely fails. It only obliquely maps to a few fields ('up to three task selections' roughly matches max_candidates=3, '95% sampling bounds' matches min_success_rate, 'independent-uniform durations' matches duration_model) while leaving required parameters items, budget, and uncertainty plus seed, samples, and allowed_tools entirely unexplained.

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 does convey a verb (compare/choose) and the core mechanism (selecting the highest-value task selection meeting a target under sampling bounds), so the general function is inferable. However, it is expressed in dense statistical jargon ('simultaneous 95% sampling bounds', 'independent-uniform durations') and never plainly says it builds/optimizes a plan from a list of candidate tasks under a budget. It also does not name or distinguish itself from close siblings like optimize_tasks, simulate_plan, or improve_schedule.

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?

There is no explicit when-to-use guidance, no statement of prerequisites, and no routing to alternatives. Given several sibling planning/optimization tools (optimize_tasks, simulate_plan, plan_mission, improve_schedule), the absence of any disambiguation is a real gap.

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