calibrated_consult_journal

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

With no annotations, the description carries the full burden, and it does disclose one genuinely useful behavioral trait: small N yields trustworthy=false, telling the agent how to interpret output. However it omits read-only/side-effect disclosure, authentication expectations, and any return-format context beyond the flag.

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?

Compact at roughly two clauses: the metric summary is front-loaded and the trustworthy=false caveat follows. Information-dense with little waste, though the heavy Russian jargon makes it slightly harder to parse on first read.

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 no-parameter, read-only summary tool with no output schema, the description does the needed work by naming the metrics returned and the trustworthy flag that qualifies them. It leaves minor ambiguity around the acronym-heavy metric names but is otherwise sufficient for correct invocation.

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 (empty schema, none required), so there is no parameter semantics to document. The baseline for a 0-parameter tool is 4.

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 resource and scope: a summary ('Сводка') of overreliance OSI metrics over the user's consults, enumerating concrete signals (confidence inflation, suppressed 'don't know', advisory-vs-independent calibration). This differentiates it from siblings like calibrated_consult_open/close/resolve and calibrate, which handle the consult lifecycle rather than reporting on it. The dense jargon slightly obscures the verb, but the intent is discernible.

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?

There is no explicit when-to-use or when-not-to-use guidance, and no alternatives are named despite the presence of closely related siblings (calibrated_consult_close, calibrate, prediction_calibration). Usage can only be inferred from the metrics listed.

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