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

Annotations declare readOnlyHint=true and openWorldHint=true, so the safety profile is already covered. The description adds meaningful behavioral context beyond those: it specifies the temporal scope ('this week'), the requirement of a 'real two-sided book', that the consensus is read 'at that exact strike', that best-price is expressed 'cents per $1 of payout', and that Kalshi prices are 'net of fee'. It also discloses that it is 'Free, no key'. No contradiction with annotations is present.

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 long but information-dense: it front-loads the core value proposition, enumerates the exact output fields, then gives filter options and concrete use cases. Every sentence contributes something meaningful, and the example queries earn their place by showing an agent how to phrase intents. It could be slightly tighter by separating the field list from usage examples, but it remains effective.

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?

Because there is no output schema, the description responsibly enumerates what the tool returns: strikes, book consensus, sportsbook lines, exchange quotes, gap in cents, and best price. It also covers filters, free access, and the key comparison intent. Minor gaps remain: it does not explain how rows are ordered, how filters combine, or what happens when no venues match a strike, but the absence of an output schema is substantially compensated by the detailed field description.

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 100%, and every parameter has its own descriptive text, so the schema already carries the full burden for parameter meaning. The description merely paraphrases the filters ('Filter by team, game, player or stat') and adds no parameter-level semantics beyond what the schema provides. This matches the baseline of 3 for high schema coverage.

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 identifies the tool as a weekly NFL player-prop price board comparing Kalshi, book consensus, DraftKings/FanDuel/BetRivers, Novig, and ProphetX. It states the specific information returned (strikes, gaps, best price per side), which distinguishes it from most sibling tools by focusing on cross-venue price comparison rather than modeling or player-level analysis. However, it lacks an explicit action verb like 'list' or 'compare', and does not directly contrast with the sibling nfl_prop_edge.

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 includes explicit 'Use for' guidance with concrete query examples like 'where is the best price on Puka Nacua receiving yards' and 'is Kalshi cheaper than the books'. This tells an agent when the tool is appropriate. It does not, however, state when not to use it or name alternative sibling tools for edge calculations or player outlooks, leaving some routing to inference.

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