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

Annotations already cover read-only, idempotent, closed-world behavior, and the description still adds real context: every figure carries its period, source and publication date, and a model's absence from the table does not mean it isn't sold. It stops short of documenting coverage periods, update cadence or freshness limits.

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 data scope is front-loaded and the two-mode logic follows immediately. The bilingual EN restatement at the end largely duplicates the Russian text, which costs some economy without adding new information.

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 an output schema present, return-shape detail is unnecessary, and the description still covers scope, both modes, data source, per-figure provenance, and the incompleteness caveat. An agent has what it needs to decide and call correctly.

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 100%, so the baseline is 3; the description earns more by explaining the consequence of supplying or omitting model (all figures vs. latest published figure per model) rather than restating the field descriptions. It doesn't add format or default details beyond what the schema already states.

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 concrete verb and resource (new EV and plug-in hybrid registrations in Russia by model), names the source (Autostat, PPK data), and spells out the two operating modes: no model returns the most-registered models, a model returns all its figures. That is enough for an agent to separate this from siblings like compare_models or get_model.

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?

Gives explicit trigger queries ("какой электромобиль продаётся лучше всего", "сколько продали X") and ties each mode to a parameter state. It never names an alternative tool or says when not to use it, so routing against compare_models/search_models is left to inference.

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