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

With no annotations, the description carries the full burden of behavioral disclosure. It discloses the weighting scheme and the output components (final score, label, eligibility flag, sub-percent breakdown), which is useful. However, it omits how labels map to score ranges, how leaderboard_eligible is determined, and whether the operation is a pure computation or has side effects.

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 compact, front-loads the output components, and then gives the exact weighting formula in a single additional sentence. Every phrase earns its place and there is no filler.

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 four-parameter computation with no annotations and no output schema, the description names the output components and the weights, which is reasonably informative. Still, it leaves significant gaps: label thresholds, leaderboard_eligible logic, the exact nature of the sub-percent breakdown, and any distinction from the v2 sibling tool.

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%, so the description must compensate. It maps all four parameters to weights (sv, sp, barrier, monetization) and clarifies their role in the composite calculation. It does not deeply explain the semantic meaning of each sub-score, but the parameter names and the weighting formula provide enough added meaning beyond the raw 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?

The description clearly states the tool's function: composing a composite LRS score from four sub-scores, with a weighted formula and specific output components. It distinguishes itself from the individual compute_* sibling tools by mentioning the inputs, but it does not differentiate itself from the closely related compute_lrs_composite_v2 sibling.

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 gives no guidance on when to use this tool versus compute_lrs_composite_v2 or the individual score calculators. The mention of weights and sub-scores implies the sub-scores should be computed first, but no conditions, exclusions, or alternatives are explicitly stated.

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