starter_allocation

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

Beyond annotations (readOnlyHint, idempotent), description adds that the mapping is a fixed, explainable rubric (never a guess) and that it is propose-only, not a forecast, providing valuable behavioral context.

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?

Concise four-sentence paragraph, front-loaded with the action, followed by essential details and exclusions. No wasteful language.

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 complexity (4 enum parameters, output schema present), the description covers inputs, mapping logic, validation step, and behavioral limitations, making it fully actionable for an agent.

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?

Despite 0% schema description coverage, the description lists all three required parameters with their enum values and explains the mapping to a posture and benchmark validation, adding meaning beyond the schema.

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?

Clearly states the tool converts user risk answers to a starting allocation, specifies inputs (three onboarding answers), and distinguishes from sibling propose_allocation by noting it uses a fixed, hand-designed rubric.

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 instructs to ask the three questions in plain language first, then call with answer tokens. Also clarifies it is not a recommendation or return forecast, and that propose_allocation is a similar tool.

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