signal_outcomes

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

Annotations already indicate readOnlyHint=true and idempotentHint=true, and the description adds substantial behavioral context beyond that: results are per scoring regime version, default to the current version, and small `total`s signal a young regime that should be flagged. It also explains the data flow (daily snapshots paired with future price moves), which is genuinely useful for interpreting results.

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 a clear purpose, then provides a concise mechanism explanation, a caveat about young regimes, and a dedicated Args section. Every sentence adds information relevant to selecting or invoking the tool, with no filler or repetition of schema fields.

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?

Given the output schema exists and there is only one optional parameter, the description is complete: it explains conceptual purpose, data provenance, per-version behavior, default parameter handling, and an interpretive caveat. No critical information needed to call the tool correctly is 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 0%, so the description carries the full explanatory burden for score_version. It does so by stating the parameter restricts results to one scoring regime and defaults to the current one. This is sufficient for a single optional parameter, though it could have added a bit more detail on how versions are identified.

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 states a specific function: computing hit-rates for every score bucket at 30/60/90 days. It explains the underlying snapshot-and-price mechanism and frames the output as a scorecard of per-label resolved snapshots and win rates. This is distinct enough to separate from sibling tools like top_signals or ticker_signal, even without naming them.

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 opening question 'Does a label actually go up?' and the description of the scorecard give clear context for when to use this tool: when an agent needs outcome validation per score label over 30/60/90 day horizons. It does not explicitly name alternatives or exclusion cases, but the use case is clearly scoped.

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