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

Annotations already declare readOnly, non-destructive, and closed-world, so safety is covered. The description adds real behavioral context: the response content, the calibration constraint (number only appears in the measured-calibrated 30–70% band), and that it discloses what the model does NOT account for — useful for interpreting output.

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 core purpose followed by a dense but informative enumeration of outputs, then trigger examples. Every clause carries information, though the parenthetical breakdown is slightly heavy for a single sentence.

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 correctly compensates by enumerating the response components (projection, verdict, gap, caveats) and the calibration caveat. An agent has enough to call it and interpret results, though pagination or scope limits (single week) are only briefly implied.

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% with both parameters fully described in the schema, including an example value for 'stat'. The description adds no syntax or format detail beyond the schema, so the baseline of 3 applies.

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?

States a specific verb (explain) and resource (why the model prices a player's Kalshi prop strikes where it does), and enumerates exactly what the explanation contains: projection, The Ladder verdict, model-vs-market gap, and known exclusions. This distinguishes it from siblings like explain_prop, explain_ladder, and explain_game.

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?

Provides explicit trigger phrasing ('why do you price <player> there', 'explain <player>'s prop') that maps user intent directly to this tool. It lacks explicit negative routing or named alternatives (e.g., 'for game-level questions use explain_game'), 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.