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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the description's remaining job is scope disclosure, and it does that well: men's only, main bracket from the round of 64, no First Four games. It also clarifies that results are counted historical facts rather than projections, which is behavioral context the annotations cannot express.

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 core scope and the three call modes are front-loaded in the first two sentences, which is the right ordering. It is slightly over-stuffed at the end, where 'not odds, not win probabilities for any future game, and not betting advice' states the same exclusion three ways.

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?

With no output schema, the description carries the burden of describing returns, and it sketches them adequately (seed line stated as wins in 160 games, typical yearly upset counts, furthest round reached, plus a whole-table mode). What is missing is finer return-shape detail such as field names or how the all-seeds table is arranged, but the dataset boundaries and invocation modes are complete for a two-optional-parameter read 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 coverage is 100%, so the baseline is 3, but the description adds real meaning the schema does not: omitting both arguments returns the whole table, and the arguments are alternative lenses on the same dataset rather than combinable filters. That clarifies the optionality semantics beyond the bare 'Optional' markers.

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 uses a specific verb ('Answers upset questions') on a precisely bounded resource: men's tournament, counted game by game, 1985-2025, 40 tournaments, 2,520 games. It also lists the concrete question types (seed-line first-round win rate, typical yearly upset count, deepest run by seed), which separates it from the sibling tools get_backtest and get_cinderella_radar.

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 says exactly how to invoke: pass a matchup ('12 vs 5'), a seed, or no argument for the whole table, which covers all three call modes. It also rules out forecast use ('not odds, not win probabilities... not betting advice'), effectively routing prediction questions elsewhere, though it never names the sibling tools it is being distinguished from.

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