compute_lrs_composite

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

With no annotations present, the description carries the full transparency burden and it does reasonably well: it discloses the output components (0-100 score, label, eligibility flag, breakdown) and the weights applied. It does not reveal label thresholds or eligibility-flag logic, but the essential computation behavior is visible.

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?

Two compact sentences deliver the key facts with no filler. The output composition is front-loaded and the weighting details are placed directly after, making the tool's behavior easy to scan.

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?

The description covers the core purpose and weights, but there is no output schema and no explanation of how labels, leaderboard eligibility, or the sub-percent breakdown are derived. Given the absence of annotations and output schema, an agent would still have to infer some important computation details.

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%, but the property names themselves are fairly self-explanatory and the min/max ranges are in the schema. The description adds meaning by listing weights ('sv 0.25, sp 0.30, barrier 0.25, monetization 0.20'), though it uses abbreviations that are not explicitly mapped to the schema property names.

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 states a clear action ('Compose') and an identifiable resource: a final LRS score with a label, eligibility flag, and breakdown. It also includes the weighting scheme, which sharpens what the tool computes. However, it does not differentiate itself from the sibling compute_lrs_composite_v2, so an agent may not know which variant to choose.

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 guidance is given about when to use this tool versus alternatives such as compute_lrs_composite_v2 or the individual component tools. The description implies the tool should be used after subscores are available, but it never states prerequisites or exclusions.

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