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

Adds substantial context beyond the readOnly/idempotent/closed-world annotations: numbers are read from judgment ledgers at request time with a 15-minute cache, cells below track_record.*.min_n show n and days but no rates, calibration is ECE with a bucket-midpoint approximation, Brier is not computed, and profit metrics are excluded. These are precisely the caveats an agent needs to avoid misreporting 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?

Front-loaded with an explicit 'Purpose' lead and sectioned as When to call / Next steps / Caveats / Output nature, so an agent can skim. It is dense and near the upper bound of useful length; the trailing disclaimer about past rates is somewhat boilerplate, keeping it just short of a 5.

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 zero-parameter status card with an output schema already defined, the description covers identity, scope, freshness semantics, sample-size thresholds, computation caveats, and plan-tier visibility. Nothing needed to call or correctly interpret the tool 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?

The tool takes zero parameters, so there is no parameter meaning to convey and the baseline of 4 applies. The description does not need to compensate for any schema 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?

States a specific deliverable: a one-page onboarding card covering identity (public beta, individual operator, no SLA), coverage (markets, bar intervals, universe sizes), data freshness per pipeline component, and measured track record with weak spots. This clearly distinguishes it from data-oriented siblings like get_market_context or get_symbol_context.

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?

Explicit sequencing instruction — 'Start here' / 'When to call: first, before any other OneQAZ tool, or when asked how reliable OneQAZ is' — plus a named next-step chain (get_daily_brief, get_market_context, get_symbol_context, get_signal_calibration, get_prediction_accuracy). The agent knows both the trigger and the follow-up routing without inference.

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