kalshi_get_orderbook

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

With no annotations, the description carries the full behavioral burden and discloses the key quirk: 'Kalshi publishes bid ladders only; asks are derived as 1 - NO bid' — revealing that asks are computed, not native — plus 'Raw ladders included' flags that both raw and normalized forms are returned. This is genuinely useful beyond the schema. Minor omissions (auth, error behavior) keep it from a 5. No annotation contradiction.

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?

Two tight sentences (~25 words) that front-load the purpose then add the essential derivation detail in a parenthetical. Every clause earns its place — including the final 'Raw ladders included,' which conveys a distinct fact. No filler or redundancy.

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?

The output schema covers return format, so the description need only handle retrieval semantics, and it delivers the critical normalization/derivation quirk. Gaps: no sibling-routing guidance and no depth semantics. Adequate for a 2-param read tool but short of fully complete.

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 0%, so the description must compensate, and it partly does: the normalization/derivation context clarifies what the ticker's ladder values represent. But it never explicitly defines `depth` (number of ladder levels) or the ticker format, relying on standard naming and the default of 10. Meaningful but incomplete compensation for the coverage gap.

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?

Description states a clear verb-resource-scope ('Kalshi orderbook for a ticker') and goes further to specify the normalized YES-book bid/ask view. It's concrete and specific. However, it doesn't differentiate from the generic `get_orderbook` sibling in the same tool set, so an agent must rely on the kalshi_ prefix to infer how the two differ.

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?

No when-to-use or when-not-to-use guidance. With both `get_orderbook` and `price_history` as siblings, the description offers no basis for choosing `kalshi_get_orderbook` over them, and never mentions alternatives or exclusion conditions. The normalization note clarifies data semantics, not tool selection.

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