calculate_expected_value

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

No annotations are provided, so the description carries the full burden. It only describes the input format ('probability:value' pairs) but does not disclose any behavioral traits like idempotency, error handling, or output characteristics beyond the existence of an output schema.

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 concise, using an emoji for visual cue, a clear purpose line, and an Args section with example. It is front-loaded and every sentence adds value. Minor improvement would be adding a brief note on expected output.

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 tool with one parameter and an output schema, the description is fairly complete. It explains the input format and provides an example. The output handling is left to the schema, which is acceptable. However, missing usage context slightly lowers completeness.

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 schema has 0% coverage (no descriptions), but the description adds significant meaning for the single parameter 'scenarios' by specifying the format ('probability:value' pairs) and providing an example ('0.7:100, 0.2:-50, 0.1:0'). This compensates well for the schema gap.

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: 'Expected Value Calculator — Objective decision-making via probability-weighted outcomes.' It uses a specific verb ('Calculate') and resource ('Expected Value'), and is distinct from sibling tools which are mostly qualitative or strategic.

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?

No explicit guidance on when to use this tool vs alternatives. The description implies usage for probability-weighted outcomes, but lacks when-not scenarios or references to sibling tools like 'bayesian_update' or 'calibration_predict'.

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