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

Annotations already establish readOnly/idempotent/non-destructive, so the description's job is to add context beyond that — and it does: daily in-season refresh cadence, data window (regular season since 2022), and a backtest error figure (3.86 vs 4.30 baseline) that communicates expected accuracy for team-changers. It does not disclose pagination or rate-limit behavior, which is the main remaining gap.

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?

Front-loaded with the index definition and metric breakdown, which is dense but earns its place by mapping to the sort and filter enums. The backtest sentence and the trailing tagline are useful framing rather than filler. Slightly long, and the three view modes are buried mid-paragraph rather than given their own line, which costs a point.

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 10-parameter, enumeration-heavy analytics tool with no output schema, the description covers the metric families returned and how the views differ, which is what an agent needs to pick parameters. It omits any mention of offset/next_offset pagination semantics and the odd 'NBA only' note on new_team_only, so it is not fully complete.

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 70% schema coverage and 10 parameters, the description goes beyond the schema by explaining the semantics of the metric fields that back sort_by (player_value, system_lift, teammate_drag, usage, trend) and the star-with/without split that backs step_up_without_star, plus the full archetype vocabulary. It adds no guidance for team, position, limit/offset, or new_team_only, so it doesn't fully compensate for the coverage 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 states a specific verb+resource (WNBA Player Index) and defines precisely what it computes — player_value, system_lift, teammate_drag, usage, trend, star with/without splits, archetype — which is far more than a restatement of the name. It also explicitly positions itself against the obvious confusion source, saying it is 'a ranking of who is real and who rides the team, not a nightly projection,' distinguishing it from invoke_wnba_projections.

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?

It tells the agent when to use each mode: view 'players' (default, pass player for one player's season history), 'systems' for team lift, 'step_up' for biggest gains when the star sits. It also states the model scope (regular season since 2022) and contradicts the projection use case. It does not name the sibling tool to use for nightly projections instead, so it falls short of an explicit alternative routing.

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