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

Beyond the readOnlyHint/openWorldHint annotations, the description discloses the exact return fields (model probability, Kalshi price, edge in percentage points, side), sort order (biggest edges first), the Pro key requirement, and the not_covered behavior for unmodeled tickers with an explicit promise never to fabricate an edge. This is strong behavioral disclosure.

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 front-loaded with the core value proposition, then each sentence adds a distinct fact: return format, auth requirement, usage triggers, and ticker behavior. The natural-language query list is slightly redundant with the market enum but still earns its place by making the tool's invocation more discoverable.

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 read-only tool with four optional parameters and no output schema, the description covers outputs, sorting, authentication, market types, ticker-specific behavior, and failure semantics. No critical operational detail appears 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?

Schema description coverage is 100%, so the baseline is 3. The description adds value by explaining the tickers[] use case — pasting a Kalshi Pro screener watchlist — and noting that it applies to futures/mvp/championship markets. It also preempts a failure mode by stating that unmodeled tickers return not_covered, which is more than the schema provides.

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 defines the tool as surfacing disagreements between the PMP NFL model and live Kalshi prices, listing concrete market categories: game moneylines, season win-total futures, MVP, and championship odds. This scope distinguishes it from broader siblings like scan_mispricings and prop-specific tools like nfl_prop_edge, even without naming them explicitly.

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 gives explicit natural-language usage triggers ('which NFL games are mispriced on Kalshi', 'NFL win total edges', 'NFL MVP value') and a clear tickers[] workflow for checking specific Kalshi markets. It does not name sibling alternatives or state when not to use this tool in favor of another, so it stops short of a 5.

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