compute_lrs_composite_v2

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

With no annotations provided, the description carries the full burden and does a strong job: it discloses exact default weights, that both weighted and equal-weight baselines are returned, that a buildComplexityPenalty is subtracted, and how an opt-in sectorProfile changes weights. It stops short of describing edge cases or exact output structure, but the key behaviors are transparent.

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 dense and mostly front-loaded, moving from purpose to weights to penalty to labels without wasted sentences. Some jargon ('SV', 'X-Signal') is compact but not fully expanded, and the run-on structure could be easier to parse, yet it remains efficient.

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 no output schema, the description explains that both weighted and equal-weight results are returned but not the exact output shape or field names. It also does not state defaults for omitted optional parameters like buildComplexityPenalty or sectorProfile, or fully enumerate the reshuffled weight sets for each sector profile. These are meaningful gaps for a calculation tool with 8 parameters.

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 coverage is only 13%, so the description must compensate, and it adds substantial meaning: it maps the six required scores to abbreviations, gives exact default weights, explains the penalty parameter, and describes the sectorProfile reshuffling behavior. It does not deeply define each score's conceptual meaning, but the parameter names plus weight assignments are largely sufficient for correct invocation.

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 computes an LRS composite v2 score from six named components, which is a specific verb+resource combination. However, it does not differentiate v2 from the sibling compute_lrs_composite, so an agent cannot tell what changed between versions from the description alone.

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 is given about when to use this tool versus alternatives like compute_lrs_composite or other composite tools. The description implies usage by defining weights and optional sector profiles, but it never states 'use this when...' or 'use X instead when...'.

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