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

Annotations only cover the safety profile (readOnly, openWorld, non-destructive); the description goes well beyond them by disclosing the deliberate methodological choices (no expected value, no intra-bucket interpolation, open-ended tail buckets) and that every returned figure traces to a live sourced price. This is exactly the kind of behavioral context annotations cannot convey.

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 purpose and method rationale, and the trailing 'Returns:' list earns its place given there is no output schema. The prose sentence before it is long and slightly dense, but no sentence is pure filler.

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 full return-value burden and does so explicitly (buckets[], medianBucketLabel, modeBucketLabel, vigPct, conditional probabilityAtOrAbovePct + snappedThresholdUsd). Combined with the when-to-use examples, an agent has everything needed to call and interpret it.

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 both parameters are already documented, including the default-BTC behavior and the threshold snapping rule. The description largely restates the schema's threshold semantics and only marginally extends it by naming snappedThresholdUsd as an output, so the baseline 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 and resource (reads Kalshi's live BTC/ETH year-end price ladder) and immediately characterizes the data as a set of mutually-exclusive prediction markets, which differentiates it from the sibling prediction-market tooling (run_prediction_market_edge, run_odds_converter). An agent can identify the tool's function without opening the schema.

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 concrete triggering user utterances ('what does the market think BTC will be worth by year end?', 'what are the odds ETH ends the year above $X?'), which gives strong when-to-use signal. It does not explicitly name or exclude alternative tools, 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.