get_power_rankings

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

All four annotation hints are false, so the description carries the full burden of behavioral disclosure, and it delivers. It discloses the side effect ('Appends calculation evidence without changing rosters'), defines the status contract (status='ok' or status='incomplete'), and documents edge-case behavior (null ranks for incomplete lineups, empty list for missing lock coverage). This is substantial transparency beyond what the schema or annotations provide.

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?

Seven sentences is on the longer side, but every sentence earns its place: purpose, prerequisites, return format, two distinct edge cases, scope clarification, and side-effect disclosure plus sibling routing. The purpose is front-loaded in the first sentence, and the final sentence efficiently routes to recommend_lineup. Slightly dense, but 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 zero-parameter tool with an output schema, this description is complete. It covers purpose, prerequisites, return values (rankings, source details, warnings, input revisions), status semantics, edge cases, scope limitations, side effects, and the alternative tool. Nothing an agent needs to invoke it correctly is missing.

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 tool has zero parameters and 100% schema coverage, so the baseline is 4. The description mentions the underlying data requirements ('weekly inputs for every team, verified locks') in prose, which orients the agent on what the tool needs at runtime even though no explicit parameters exist. No parameter documentation is necessary, and none is missing.

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 opens with a specific verb-resource pair: 'Compare every team's best legal weekly lineup by projected points.' It states scope (every team), what is compared (best legal weekly lineup), and the metric (projected points). It also distinguishes itself from the sibling recommend_lineup, which analyzes only the selected team, so an agent can tell them apart immediately.

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 explicit prerequisites: 'Requires current season data, weekly inputs for every team, and verified locks with league scope.' It names the alternative explicitly: 'Use recommend_lineup to analyze only the selected team.' It also clarifies scope boundaries — these are one-week estimates, not championship probabilities — so an agent knows when not to over-interpret the result.

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