starter_allocation

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

Annotations already indicate readOnly=true and idempotent=true. The description adds that the mapping uses a fixed rubric (never a guess) and returns a preset allocation validated against a benchmark. It explicitly states it is not a recommendation or forecast, which is valuable behavioral context beyond annotations.

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 three sentences, no wasted words. It front-loads the purpose, then details parameters, then provides usage caveats and disclaimers. Every sentence adds value.

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 moderate complexity (4 enum parameters) and existence of an output schema, the description covers inputs, mapping logic, validation, benchmark options, and workflow. It also disclaims recommendations. This is complete for an AI agent to invoke the tool correctly.

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?

With 0% schema description coverage, the description fully documents all 4 parameters by listing their enum values and explaining their meaning (e.g., horizon, loss_response, cash_buffer, and benchmark with default). This compensates entirely for the missing schema descriptions.

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 purpose: mapping new user risk answers (horizon, loss_response, cash_buffer) to a starting allocation. It specifies the verb 'Turn' and resource 'risk answers into starting allocation', distinguishing it from siblings like propose_allocation by noting it's for new users and uses a fixed 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?

The description provides clear workflow guidance: ask questions first then call with answer tokens. It mentions propose_allocation as similar but does not explicitly state when to use one over the other. However, the context of 'new user' implies onboarding, so usage is well-defined.

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